The ethics of emotion-arousing & psychoactive ads and their influence Essay

The ethics of emotion-arousing & psychoactive ads and their influence on individuals - Essay Example they depend upon the fact that prospective customers would be interested in obtaining more information about the product and services so that they can arrive at the logically best choice. On the other hand, an emotional appeal is used to create an ambience, to invoke a general feeling - of goodwill or of fear or urgency or any other diverse emotions. It is expected that the aroused feelings would incite and encourage the prospective customer to buy the products associated with such ads. While the appeal of both the approaches has been established by several studies, some professional critics believe advertising adversely affects people and manipulates us to buy things/services by playing on our emotions. Advertising is so powerful that consumers are helpless to defend themselves against it. This has been a time long debate involving issues like not only the morality of using emotions to manipulate consumers, but also about the efficacy and potency of such ads to actually influence the purchase decision. Emotional advertising readily gains public attention when it evokes our fears and wants, sometimes at a very deep level. Some critics claim that these powerful messages are often ineffective, because consumers often tend to remember the emotions but not the product (O’Neill, 2006). The subject under consideration is vast, as scores of early researchers have tried to find evidence for the harmful effects that advertising, and especially emotional advertising has had on people o different ages. Further, an equal number of organizations and businesses have supported research extolling the powerful impacts of emotional advertising and thus providing them with the rationale of spending millions of pounds into emotional advertising. The current research will therefore narrow with scope to a more specific topic – and try to evaluate the relative effectiveness of two different kinds of emotional appeals, namely of using warmth and of using fear in ads. The

Sam Houston Summary Essay Example for Free

Sam Houston Summary Essay Sam Houston had to face many obstacles since he was a little boy. He had very little education; however he educated himself with many readings from his father’s library. Houston served as governor for Tennessee and Texas. He also served as a United States senator for thirteen years. . He befriended General Andrew Jackson and remarried three times. Houston was made the first president of the Republic of Texas in 1836 and was re-elected in 1841. He gave many speeches throughout the years. Houston died on July 26, 1863, in Huntsville, Texas. Sam Houston, fifth child of Samuel Houston and Elizabeth Paxton Houston, was born on March 2, 1793. His father, Samuel Houston, a member of the revolutionary member, went bankrupt in 1806 and had no other option but to sell the Timber Ridge and move west to Tennessee. His father died the same year! A year later, Sam Houston, his mother, and his eight siblings resided south of Knoxville in Maryville, Tennessee. At the age of fourteen, Sam Houston had little education but spent a large amount of time reading books in his late father’s library. He also spent a great amount of time clearing land and developing a farm. The family decided that he would work as a clerk in a store because in his brother’s eyes he wasn’t doing anything worthwhile. Sam showed no interest in this job which resulted in his disappearance from the job and home. He crossed the Tennessee River with the Cherokee Indians. Houston joined a band of approximately three hundred Cherokees led by Chief Oo-loo-te-ka. The chief liked Sam greatly that he soon adopted him and gave him the name â€Å"Colonnen†. Colonnen refers to â€Å"The Raven† which is a symbol of good luck to the Indians. He went into debt due to bringing gifts home to his Indian friends each time he went to visit his family. At the age of nineteen, Houston had to find a way to get out of debt and decided to become a teacher. He charged the students a higher rate than usual which only took six months to pay off his debts. At the beginning it was just a way to make money but he soon realized that he had a strong interest in teaching. The War of 1812 had begun shortly after Houston began teaching. His career ended really soon. â€Å"Euclid soon defeated him and ended whatever thoughts he may have had of a permanent careers as a teacher. Houston was not amongst the early volunteers of the war; however he decided to enroll in a local academy to further his education in math. On March 24, 2013, Sam decided to enlist in the United States Army. Within a few weeks of training he became a sergeant. Nine months later, Sam was promoted to third lieutenant. Early in the 1814, Houston came under command of General Andrew Jackson. He became really good friends with Jackson for approximately thirty years. During the war, Houston was wounded and General Jackson ordered him to stay out the rest of the battle. The Indians refused to surrender. Houston volunteered to lead the attack; however his men hesitated to go forth, Houston ran forward and the Indians ended up shooting him twice in the right shoulder. Jackson ordered the Cherokees to complete the battles which forced the Indians to sign the Treaty of Fort Jackson. This treaty consisted of giving up their claims to more than three-fifths of Alabama. Sam Houston was treated at one of their hospitals in eastern Tennessee and then transported back to Maryville. After the treatment he was assigned to the Southern Division of the United States Army. He was on light duty in the adjutant general’s office. On October 28, 1817 Houston was appointed federal subjacent to the Cherokees. At the age of twenty-five, on March 1, 1818 he resigned from the army and as Indian subagent too. Three months later, Sam Houston returned to Nashville to begin the study of law with Judge James Trimble. It only took Houston six months to learn and pass the bar examination. He then moved to Lebanon and opened a law office. In 1819, Houston was elected attorney general, and he then returned to Nashville. He was very successful and knowledgeable but he was not satisfied with the pay. He resigned in1820 in private practice in Nashville. â€Å"In 1823 he ran for the United States House of Representatives from the ninth Tennessee District. † (pg 15) Houston’s second term ended on March 1827, he went back to Tennessee to run for governor. He put himself in the public’s eyes by attending political rallies and any events that included the voters. â€Å"In early September he defeated Cannon by more than 11,000 votes in a total of more than 75,000 and won the governorship. † (19) In 1824 Sam Houston was introduced to a woman named Eliza Allen. She was only twenty and Sam Houston was thirty-five at the time. He asked her father for his permission to marry his daughter. Her father gave him permission to wed his daughter. She did not love Sam; however her family pretty much pressured her into marrying Houston only because he was a successful man. She followed their command and accepted his proposal in October 1828. They exchanged wedding vows on January 22, 1829 at the Allen’s home. Unfortunately, three months after the marriage his wife left him and went back home to her family. She refused to be with him and on April 16th Houston resigned as governor of Tennessee. It is still unknown why she left Houston, although she was pressured into marrying Houston. Sam and his companion, H.Haralson, journeyed down the Cucumber River to Ohio on April 23, 1829. He ended up in Arkansas with his Cherokee friends. His companion continued to travel leaving Houston behind with his adopted father and friends. Sam Houston soon discovered that the Cherokee nation had to move out of Arkansas and into east-central Oklahoma. The Cherokees were worried that the whites were going to take their current land again. Houston became their advisor and a listener to the Cherokee tribe. â€Å"Houston departed to Washington to inform President Jackson that several Indian agents should be removed and the Treaty of 1828 honored. He traveled to Washington D. C. to inform how the Cherokees received unbroken promises and that they rather have money verses gold. Sam Houston wed another woman, Tiana, while still married to Eliza. It wasn’t a big deal because she was considered Indian and his other wife was Cherokee. Within time he got a weakness for alcohol. It started interfering with his everyday live, was turned down when he campaigned for a position on the Cherokee council, and he also hit his adoptive father. Soon after, his mother fell ill and was rushed back to her home in Tennessee where she died in September 1831. A month later he traveled back to the Cherokee nation. In April he was arrested and punished for striking congressman, Stanbery, with his cane. He was found guilty and received a reprimand. In October 1832, Houston started preparing for his trip to Texas; however his wife refused to go. Houston left her some of his possessions and then divorced her. Sam Houston set up headquarters in San Felipe and began to plan a defensive strategy against the Mexicans. The volunteer soldiers under Stephen Austin battled and successfully took San Antonio, killed Milam, and forcing General Cos to surrender. The soldiers believed that the war was over; however Houston was not convinced that it was over. He made a public announcement that Texas was in need of 5,000 trained men by March 1, 1836. While Houston and the men prepared for the war they were approached by Fannin and the two leaders that won at war in San Antonio. Fannin, Johnson, and Grant came in making promises to the soldiers causing thousands to leave Houston and to join their militia. Sam Houston continued to lead approximately sixty to seventy men. On January 20, Houston traveled to San Felipe to meet with Governor Smith. Houston spent the rest of February with the Cherokees and Indians in the northeast. After arriving at the convention site, Washington-on-the-Brazos, he received news that Santa Anna’s army had attacked Texas and â€Å"besieged† the Alamo and the remaining soldiers. The declaration of independence was adopted on March 2nd. Two days later, Houston had become â€Å"commander in chief of the land forces of the Texian army both Regulars, Volunteers, and Militia, while in actual service. † On March 11th, Houston reached Gonzalez and found 374 volunteers that were led by Edward Burleson. While preparing the men for war Houston received news that Santa Anna’s army had â€Å"taken† the Alamo and attacked and burned all of its defenders. Houston ordered Fannin and his army to retreat to Victoria. They were captured and murdered by the Santa Anna’s army. On April 20th, the battle began between Santa Anna and Houston’s soldiers. Unfortunately, Houston was injured during the battle. Houston’s army men had captured and killed many men causing Santa Anna to offer a peace treaty. Houston refused until both government leaders were present. Fortunately, Houston’s approach succeeded. Sam Houston and Eliza’s divorce was not final until 1837. Houston served as president for two years, December 1836 to December 1838. He was reelected in September 1840. In 1836, a small group of soldiers were attacked by the Mexican General Santa Anna. Houston’s army won the battle against the Mexican forces at San Jacinto and gained independence for Texas, opened up a law office, and promoted a land development after his term expired. He traveled to Mobile, Alabama to interest a wealthy merchant, William Bledsoe, in the Sabine City project. While visiting the Bledsoe estate Houston met Margaret Lea. They were married in Marion, Alabama, on May 9, 1840. They had eight children. Margaret convinced Houston to stop drinking and attend church. Houston continued to work towards annexation with the United States. He used the United States and Great Britain hatred to one another in hopes to make each country want to snatch up Texas so that the other country could not. With high hopes of joining with the United States, the United States still was unwillingly to annex Texas. In 1845, Texas became part of the United States. â€Å"Houston’s joyous moment at the approach of annexation was tempered by the death of Andrew Jackson on June 8, 1845. The family rushed to the Hermitage but arrived a few hours late. They attended the funeral and were guests at the Donelson plantation for several weeks. † (139) Meanwhile, Mexico was at war with Texas for ten years. Houston remained in Washington to work in support of the war. Houston left Washington in the spring of 1847 because his wife had surgery due to breast cancer. â€Å"Houston traded Raven Hill for land within a few miles of Huntsville and planned to build a home for his family. He remained in Texas for the rest of the year, keeping abreast of the war news as General Winfield’s Scott’s army took Mexico City. † (147) In January 1847, Sam Houston obtained a new six year senate term. Houston became a presidential candidate but Houston’s mind and heart was with his family. In 1852, Franklin Pierce was elected; however if Houston put in the effort he could have won. January 15, 1853, he was elected to a new six-year term as Senate. In October 1853, Houston’s family moved to Independence, a city fifty miles to the southwest, while leasing out his home in Huntsville. In 1859, Sam Houston appeared to be leaning toward retirement. Houston invited the public to vote in his favor; however he did not campaign. â€Å"Texans who wanted his leadership had drawn him into the contest, and they did the campaigning. Houston became the only man in the United States to serve as governor of two states. He promised funds for railroads, schools, river improvements, and a protector of Texas if Mexico should try to battle again. Houston was all about the people he served and not the political party. Some petitioned for him to run for president, but he refused to participate in the national convention. When Abraham Lincoln was elected president of the United States, Houston warned Texans that the civil war was going to happen if Lincoln violated the Constitution. The Texas convention removed Houston from office and replaced him with Lieutenant Governor Edward Clark. This resulted because Houston refused to take oath to loyalty to the newly formed Confederate States of America. He wanted to avoid war at all cost and declined Lincoln’s offer to use the federal troops to keep him in office and Texas in the Union. After leaving the Governor’s mansion, he continued to support Texas. Sam Jr. joined the Confederate Army, against his father’s advice, was wounded at the battle of Shiloh. He was reunited with his family but on crutches. Houston moved his family to Huntsville because United States â€Å"took† Galveston and â€Å"destroyed the Houston’s family’s main source of income. † (197) In the winter of 1863, Houston fell ill. He developed an awful cough and was diagnosed with pneumonia. His wife stayed by his side and heard his very last words before his death, â€Å"Texas†¦Texas†¦Margaret. † Texas and his family were very important to Sam Houston. Sam Houston, one of the most important political figures to Texas. Houston served as governor for Tennessee and Texas. He also served as a United States senator for thirteen years. . He befriended General Andrew Jackson. He remarried three times; however he had eight children with his third wife. Houston was made the first president of the Republic of Texas in 1836 and was re-elected in 1841. He gave many speeches throughout the years. He led a successful battle for Texas Independence. On July 26, 1863, Houston died of pneumonia in their â€Å"Steamboat House. † He will always be remembered!

The Anaconda Plan Essay example -- essays research papers

The Anaconda Plan At the onset of the Civil War, President Abraham Lincoln met with his generals to devise a strategy by which the rebellious states of the Confederacy could be brought back into the Union. General Winfield Scott, commanding general of the Union army, proposed a plan of battle that became known as the Anaconda Plan. General Winfield Scott, commanding general of the Union Army From the Collections of The Mariners' Museum General Scott, a native Virginian, believed that the majority of Southerners desired a complete union with the United States. In order to restore the Union with as little bloodshed as possible, he favored a relatively nonaggressive policy. The primary strategy of Scott's plan was to create a complete naval blockade of the Southern states. Named for the South American snake that kills its prey by strangulation, Scott's plan was to strangle the South into submission by cutting its supply lines to the outside world. The plan was sound, but ambitious. For the plan to succeed, it would be necessary to blockade more than 3,500 miles of coast from Virginia to Mexico and up the Mississippi from New Orleans to New Madrid Bend. And the Anaconda Plan could only succeed over time: the South would not starve overnight, so patience was an essential part of Scott's strategy. Gideon Welles, Secretary of the Navy, USA From the Collections of The Mariners' Museum By adopting the Anaconda Plan, Lincoln ran the risk of committing diplomatic suicide. Sin...

Hobbies: Fruit and Idle Mind Essay

Hobbies are leisure time activities. They are gardening, reading books, stamp collecting, learning musical instrument such as Veena, Violin, Guitar etc., painting, photography, bee-keeping, poultry-farming, and writing stories or novels. Hobbies are for recreation and relief from routine, stereo typed and monotonous work. They help us to develop our manual skill. They kindle one’s imagination and make one give vent to one’s latent talents. They make brisk and some of them benefit us monetarily. Everyone should have a hobby. W.H.Davies, the poet writes ‘What is this life full of care, we have no time to stand and stare’. Further an idle mind is a devil’s workshop. One must choose a hobby according to one’s tastes. They keep one engaged. They educate and help us learn many things. Some times hobbies become one’s full time profession and in a high position. Hobbies make us brisk both physically and mentally. Hobbies like stamp collection and coin collection make us rich also. So everyone should have a valuable hobby. My hobby is gardening. My father has constructed a house in one ground, there is half a ground place for gardening around the house. There is a well at the back of the house. My father advised me one day that an idle mind devil’s workshop and I should educate me and benefit me in future. He asked me to look after the garden. After my school hours, I engage myself in gardening. I will do work such as purchasing proper seeds, sowing, weeding, watching the plant, pruning, and making channels for water. This hobby has taught me the features of the plant, and their habits and my teacher would give me suggestions as times about it. Now I have grown trees such as mango, neem, coconut, banana, guava, jack and pomegranate, plants like brinjal, tomato, ladies finger and flower plants like Rose, Jasmine, and Kanakambaram. I usually sell the fruits and vegetables to my neighbours. They gives me money. I save it in small savings scheme. My father has said that it would be helpful for my higher studies. I am happy, doing it.

CIPP Diploma in Payroll Management-Work Based Essay

It was concluded that the flexible enefits choices project will benefit from the collaboration between the researcher, the company and the supervisor. The nature of the researcher’s role within the Company will ensure that access to, and the collection of information is within the capacity of the researcher. Aims The aim of this project is to evaluate the existing choices within the workplace and from the findings of the evaluation will conclude whether to keep existing choices or Introduce new choices to the companys flexible benefit scheme. bjectives The objective of this project is to review the existing benefit choices In time for the ext enrolment process for Flexible Benefit and to determine whether the choices are still meeting the personal needs of the employees. A questionnaire will be sent to all employees and based on the answers a focus group will be set up. The questionnaire and focus group feedback wlll provide the data necessary to amend or keep the choices avai lable. The project and the eventual recommendations will be feasible and do have a chance of being implemented. The issue of choices within the flexible benefit scheme is an organisational issue and are reviewed annually in readiness for pen enrolment process. However, the review has not been on the same in-depth scale that this project is proposing. The researcher In collaboration with the HR Manager will have the necessary resources to complete the project. Questionnaires will be devised and given to the employees. Email addresses are available to the researcher and the research Itself is a task that needs to be implemented. The researcher’s current position in the Company will help with the confidential aspect of a research as well as galnlng the trust of the participants Involved. The Gantt chart hows the ideal planning schedule. The renewal process for making flexible benefit choices occur in December, for the January admissions. The researcher proposes that the questionnaires and Focus group are held after the current enrolment process. The researcher also proposes that the write up and analysis occur Immediately after. This will enable the researcher to address any Issues that arises. Content The Company a financial organisation and is a moderately sized company consisting of two hundred and thirty staff on the payroll. The Company can be separated into wo groups 0T employees; DroKers ana non DroKers (aamlnlstratlon/l I ) I ne company HR and payroll is administered by a team of three consisting of the Payroll Administrator, HR Manager and the HR Administrator. Rationale The research for this project will investigate employees’ views on the existing flexible benefit choices. When the scheme was in the initial research stage, the feedbacks from the focus groups were positive and indicated that the most appreciated benefits within flex were; Holiday buy/sell Dental insurance Pension enhancement Medical insurance After two years into the scheme, the most popular flex choice is the Private medical insurance, with only 20% of employees opting for this. This research will try to investigate whether the current flex choices are meeting the personal needs of the employees. Can new choices maximise Income Tax and National Insurance efficiency for both the Company and individual employees? Last year, the change of benefit choices was put forward to the employees via an email questionnaire. However, the responses were very low and so no changes were made. This project ill provide an opportunity for an in-depth questioning of the employees with results that may determine the choices for the next renewal process. Reading The secondary research will include books and articles that are relevant to the project issue. The following Journals have shown key information in regards to flexible benefits; Bradford, S. , 2010. Flexible Benefits.

Market Entry Strategies

Market Entry Strategies How to launch the service Choosing an effective market entry strategy depends on a number of factors such as product and positioning portfolio practiced by other competitors in the industry (Blythe Zimmerman 2005, p.118). Home from Home Cooking can launch its new service by introducing the services at a reduced rate.Advertising We will write a custom report sample on Market Entry Strategies specifically for you for only $16.05 $11/page Learn More This makes it easier for the business to penetrate the market by attracting new customers who prefer purchasing low priced products and services. Considering that there are many competitors in the industry, Home from Home Cooking should have prices lower than that of its direct competitors. To achieve this, Home from Home Cooking should develop efficient production techniques capable of reducing the overall cost of production. This is from the fact that, competitors might also opt to reduce their prices in order to maintain their customers. However, the competitors cannot manage to do so if the cost of production is higher compared to that of home from home cooking. This explains why the business should struggle at having lower production costs. The company should also focus of making improvements on the current products or service offering by the competitors. Apart from relying only on young families and professional people as its main customers, Home from Home Cooking should also enhance or position its product such that the smaller consumer segments can get attracted to the services offered by the company. External sources that will help launch the service The company can rely on a number of external sources to launch the new service. For example, the business can use sites like www.moneysupermarket.com (Agriculture and Agri-Food Canada 2011, p.6). This site enables potential customers to make price comparisons. Thus, Home from Home Cooking should always ensure that the prices of the servi ces offered are slightly lower in relation to the competitors’ prices found at the site. The company should also make extensive use of other sites like www.toptable.com that provide customers with information on the state of the service offering by the business (Agriculture and Agri-Food Canada 2011, p.7). Identification of helpful wholesalers Home from Home Cooking can collaborate with locally available producers like Osterley’s farmers market. Additionally, the business should develop good relations with large supermarkets like Tesco. This will provide mutual benefit to both companies. Home from Home Cooking will benefit by experiencing increased demand. However, Tesco will get an opportunity of stocking a wider variety of British classics and eventually generate more sales.Advertising Looking for report on business economics? Let's see if we can help you! Get your first paper with 15% OFF Learn More Partners to help gain market entry Local b usiness providers like GlaxoSmithKline and the British Sky Broadcasting can offer significant benefits to Home from Home Cooking, even as the new company struggles to gain entry to the market. The new business should also partner with television stations like BBC so that it becomes easier to advertise company’s products and services through popular television programs like â€Å"The Great British Food Revival†. Promoting the business Home from Home Cooking can use different tactics to promote its services. For example, the business can place advertisements in food magazine and also distribute the company’s business cards to potential customers. This will not only inform customers about the products and services offered by the business, but will also provide them with business contacts. Launch evening with food tasting and wine When launching the new business, Home from Home Cooking should ensure that food and drinks are of good taste. This will enable the compan y to witness high rate of customer return even after launching the service. As a result, the business will also manage to maintain its market share and survive the increasing competition. References Agriculture and Agri-Food Canada 2011, The United Kingdom: A diverse Foodservice  Sector. Web. Blythe, J., Zimmerman, AS 2005, Business-to-business marketing management: a  global perspective, Thomson Learning, London.

Black History Month Creation and Overview

Black History Month Creation and Overview Black History Month is a month set aside to learn, honor, and celebrate the achievements of black men and women throughout history. Since its inception, Black History Month has always been celebrated in February. Find out how Black History Month originated, why February was chosen, and what the annual theme for Black History Month is for this year. Origins of Black History Month The origins of Black History Month can be traced back to a man named Carter G. Woodson (1875–1950). Woodson, the son of former slaves, was an amazing man in his own right. Since his family was too poor to send him to school as a child, he taught himself the basics of a school education. At age 20, Woodson was finally able to attend high school, which he completed in just two years. Woodson then went on to earn a bachelors and masters degree from the University of Chicago. In 1912, Woodson became only the second African American to earn a doctorate from Harvard University (W.E.B. Du Bois was the first). Woodson used his hard-earned education to teach. He taught both in public schools and at Howard University. Three years after earning his doctorate, Woodson made a trip that had a great impact on him. In 1915, he traveled to Chicago to participate in a three-week celebration of the 50th anniversary of the end of slavery. The excitement and enthusiasm generated by the events inspired Woodson to continue the study of black history year-round. Before leaving Chicago, Woodson and four others created the Association for the Study of Negro Life and History (ASNLH) on September 9, 1915. The following year, the ASNLH began publication of the Journal of Negro History. Woodson realized that most textbooks at the time ignored the history and achievements of blacks. Thus, in addition to the journal, he wanted to find a way to encourage interest and study of black history. In 1926, Woodson promoted the idea of a Negro History Week, which was to be held during the second week of February. The idea caught on quickly and Negro History Week was soon celebrated around the United States. With a high demand for study materials, the ASNLH began to produce pictures, posters, and lesson plans to help teachers bring Negro History Week into schools. In 1937, the ASNLH also began producing the Negro History Bulletin, which focused on an annual theme for Negro History Week. In 1976, the 50th anniversary of the beginning of Negro History Week and the bicentennial of the United States independence, Black History Week was expanded to Black History Month. Ever since then, Black History Month has been celebrated in February around the country. When Is Black History Month? Woodson chose the second week of February to celebrate Negro History Week because that week included the birthdays of two important men: President Abraham Lincoln (February 12) and Frederick Douglass (February 14). When Negro History Week turned into Black History Month in 1976, the celebrations during the second week of February expanded to the entire month of February. What Is the Theme for This Years Black History Month? Since its inception in 1926, Negro History Week and Black History Month have been given annual themes. The first annual theme was simply, The Negro in History, but since then the themes have grown more specific. Here is a list of the most current and future themes for Black History Month. 2014 - Civil Rights in America2015 - A Century of Black Life, History, and Culture2016 - Hallowed Grounds: Sites of African American Memory2017 - The Crisis in Black Education2018 - African Americans in Times of War2019 - Black Migrations

Great Railroad Strike of 1877

Great Railroad Strike of 1877 The Great Railroad Strike of 1877 began with a work stoppage by railroad employees in West Virginia who were protesting a reduction in their wages. And that seemingly isolated  incident quickly turned into a national movement. Railroad workers walked off the job in  other states and seriously disrupted  commerce  in the East and Midwest. The strikes were ended within a few weeks, but not before major incidents of vandalism and violence. The Great Strike marked the first time the federal government called out troops to quell a labor dispute. In messages sent to President Rutherford B. Hayes, local officials referred to what was happening as â€Å"an insurrection.† The violent incidents were the worst civil disturbances since the Draft Riots which had brought some of the violence of the Civil War into the streets of New York City  14 years earlier. One legacy of the labor unrest in the summer of 1877 still exists in the form of landmark buildings in some American cities. The trend of building immense fortress-like armories was inspired by the battles between striking railroad workers and soldiers. Beginning of the Great Strike The strike began in Martinsburg, West Virginia,  on July 16, 1877, after workers of the Baltimore and Ohio Railroad were informed that their pay would be cut 10 percent. Workers grumbled about the loss of income in small groups, and by the end of the day railroad firemen began walking off the job. Steam locomotives could not run without the firemen, and dozens of trains were idled. By the next day it was apparent that the railroad was essentially shut down and the governor of West Virginia began to ask for federal help to break the strike. Approximately 400 troops were dispatched to Martinsburg, where they scattered protesters by brandishing bayonets. Some soldiers managed to drive some of the trains, but the strike was far from over. In fact, it began to spread. As the strike was starting in West Virginia, workers for the Baltimore and Ohio Railroad had begun walking  off the job in Baltimore, Maryland. On July 17, 1877, news of the strike was already the lead story in New York City newspapers. The New York Times coverage, on its front page, included  the dismissive headline: Foolish Firemen and Brakemen on the Baltimore and Ohio Road Cause of the Trouble. The position of the newspaper was that lower wages and adjustments in working conditions were necessary. The country was, at the time, still stuck in an economic depression which had been triggered originally by the Panic of 1873. Violence Spread Within days, on July 19, 1877, workers on another line, the Pennsylvania Railroad, struck in Pittsburgh, Pennsylvania. With the local militia sympathetic to the strikers, 600 federal troops from Philadelphia were sent to break up protests. The troops arrived in Pittsburgh, faced off with local residents, and ultimately fired into crowds of protesters, killing 26 and wounding many more. The crowd erupted  in a frenzy, and trains and buildings were burned. Summing it up a few days later, on July 23, 1877, the New York Tribune, one of the nations most influential newspapers, headlined a front-page  story The Labor War. The account of the fighting in Pittsburgh was chilling, as  it described federal troops unleashing volleys of rifle fire at civilian crowds. As word of the shooting had spread through Pittsburgh, local citizens rushed to the scene. The outraged mob set fires and destroyed several dozen buildings belonging to the Pennsylvania Railroad. The New York Tribune reported: The mob then began a career of destruction, in which they robbed and burned all the cars, depots, and buildings of the Pennsylvania Railroad for three miles, destroying millions of dollars worth of property. The number of killed and wounded during the fighting is not known, but it is believed to be in the hundreds. End of the Strike President Hayes, receiving pleas from  several governors, began moving troops from forts on the East Coast toward railroad towns such as Pittsburgh and Baltimore. Over the course of about two weeks the strikes were ended and  workers returned to their jobs. During the Great Strike it was estimated that 10,000 workers had walked off their jobs. About a hundred strikers had been killed.   In the immediate aftermath of the strike the railroads began to forbid union activity. Spies were used to ferret out union organizers so they could be fired. And workers were forced to sign yellow dog contracts that disallowed joining a union. And in the nations cities a trend developed of building enormous armories that could serve as fortresses during periods of urban fighting. Some massive armories from that period still stand, often restored as civic landmarks. The Great Strike was, at the time, a setback for workers. But the awareness it brought to American labor problems resonated for years. Labor organizers learned many valuable lessons from the experiences of the summer of 1877. In a sense, the scale of the activity surrounding the Great Strike indicated that there was a desire for a widespread movement to secure workers rights. And the work stoppages and fighting in the summer of 1877 would be  a major event in the history of American labor. Sources: Le Blanc, Paul. Railroad Strike of 1877. St. James Encyclopedia of Labor History Worldwide, edited by Neil Schlager, vol. 2, St. James Press, 2004, pp. 163-166. Gale Virtual Reference Library. Great Railroad Strike of 1877. Gale Encyclopedia of U.S. Economic History, edited by Thomas Carson and Mary Bonk, vol. 1, Gale, 1999, pp. 400-402. Gale Virtual Reference Library.

Blogging and Privacy Essay Example | Topics and Well Written Essays - 1250 words

Blogging and Privacy - Essay Example Ellen Simonetti (2004), a Delta Air Lines flight attendant, for example, was fired after she posted some problematic photos of herself in uniform on her blog. Simonetti might want to show her blogger friends her daily life as a flight attendant through those pictures, but unfortunately, her pictures became a reason for losing her job. In general, visiting Simonetti's blog and looking up her pictures are exactly what most bloggers usually do. Yet, if someone would report her pictures to somebody without her permission, and so Simonetti might lose her job, this is certainly a violation of her privacy. If so, in what ways might Simonetti protect her personal information and privacy Regardless of any possible personal security issue, the number of blogs is still steadily increasing, and the purposes of using blogs is becoming more diversified from one blog to another blog. Hence, taking such diversity into account, bloggers must carefully consider the level of disclosure of personal information based on the content posted and the targeted audience. The rest of this analytic argumentative paper is organized as following. In the first two sections, two opposite tendencies of current bloggers towards their personal information, such as disclosing versus anonymity or hiding, are discussed with benefits and disadvantages. Then, in the next paragraph, it suggested way to efficiently manage such personal information problem in blogging will be addressed. Lastly, another helpful suggestion is explained while concluding this essay. Most of the bloggers reveal their personal information about themselves on blogs. Especially, young generation has a propensity to disclose their personal information. According to a research conducted by David A. Huffaker, Ph.D. student at Northwestern University, and Sandra L. Calvert, professor of Psychology at Georgetown University," Many teen bloggers expose their first name (70%), age (67%), and contact information (61%) as an email address" (2005, para.10). The reason why teen bloggers reveal such personal information often seems unintentional. This is just because their purpose of maintaining blogs is often to establish a close relationship with other bloggers. By sharing such type of personal information given above, the teen bloggers match the age groups that they want, and keep in touch with each other outside the blogs, too. From this point of view, blogs are an extension of the real world to teenagers in which they make more friends and share a matter of daily life. In addition to disclosing personal information, however, they also post diaries and photos which altogether have a chance of causing a security problem as which happened to Simonetti, a fired stewardess mentioned above. In regard to this issue, Mr. Henry G. Rhone, a vice provost at Virginia Commonwealth University, asserted that "[teen bloggers] have never had any problems with [their personal information and contents on their blogs], so they just assume that when they're online, they're safe" (quoted in Read, 2006, para. 17). As Mr. Rhone pointed out, Simonetti's case is rarely happened phenomenon for bloggers to realize its

Psychology. Creating a Personal Counselling Theory Essay

Psychology. Creating a Personal Counselling Theory - Essay Example Based on these 13 dominant counselling theories, I have formed my own personal counselling framework. My personal counselling theory is comprised of two of these dominant theories: Albert Ellis' rational emotive behavioral therapy (REBT) and Alfred Adler's individual psychology. These two theories hold similar viewpoints in regard to defining, describing, identifying, explaining, and changing behavior. The context of both theories explained by Mosak and Ellis (as cited in Corsini & Wedding, 2005) stressed that Ellis believed emotional disturbances resulted by the person's view on the situation, and that his type of therapy, the therapeutic process, with one main intervention technique would change irrational beliefs into rational ones. Adler, also believed emotional disturbances resulted by inferiority feelings and his type of therapy, individual psychology, with various intervention techniques would encourage social interest. According to the "Nature of Theory" (n.d.) article there are four primary elements of a good theory: Philosophical, descriptive, prescriptive and evaluative elements. This paper will examine each element in regard to Adler's and Ellis' theories concluding with my personal views. This paper will identify and justify the theoretical frameworks that make sense to me and will integrate them into a cohesive personal theory. My personal way of understanding and describing the human condition and facilitating change will also be discussed. More specifically, this paper contains five sections, the introduction, the philosophical assumptions, the counselling experience, reflections and the closing. The First section, the philosophical assumptions, will discuss my philosophical assumptions as it relates to my theory. I will provide my personal views on the nature of humans, the nature of well adjusted functioning, the major causes of the problems, and the nature of change. I will incorporate my theories view on each level, and discuss how it relates to the four elements of a good theory. The second section, the counselling experience is comprised of two topics: my definition of counselling, and the process of beliefs limited to the counsellor client relationship. The first topic will include my original and revised definition of counselling. The second topic will be broken down into six subsections including counsellor and client roles, session structure, emphasis on past, present and future, emphasis on beliefs, the relationship of behaviors and emotions, the change process including resistance, interventions, the criteria and definition of success and contextual factors. In th e six subsections, I will incorporate my personal beliefs and reflections including strengths and weakness I may have in the area, my personal counselling theories stance in that area, and how it relates to the elements of a good theory. The third section will reflect on the limitations of my personal theory and explain why I am drawn to this theory from a professional and personal context. The last section will

Quotation Essay Example | Topics and Well Written Essays - 750 words - 1

Quotation - Essay Example Actions, which might threaten or lead to loss of life contradict the right, and thus need condemnation (Freeman 5). All constitutions of the world recognize and respect the right to life and that any person who attempts or violates it faces stiff penalty. For instance, when a person kills another person, he faces life sentence for violating the right of the deceased to live. This is evident in several countries such as in the Middle East whereby tyrants who had been accused of mass murder of human beings got punished. Former Iraq president, Saddam Hussein was hanged for killing many people during his reign (Freeman 5). Therefore, since nobody provided right to life to a fellow being, he or she has no right of taking such right from another being by killing or threatening. However, there are some instances when such right is denied or when a person accused of killing a fellow human being is pardoned (Freeman 9). Such situation arises when the accused was trying to defend himself or he rself from murder by the deceased. This normally happen when in times of conflict such as war or when a person turns wild and desires to kill. Moreover, when a person kills the other, and thus turns to be a security threat to public, police officers kill him (Freeman 9). A good instance occurs during mass shootings which have characterized the U.S.A. and which have left many people grieving from loss of loved ones. A recent incident was the US school shooting in Brooklyn, Newtown. Consequently, all constitutions on earth recognize and respect the right to liberty of all human beings. People have a right to be free from all forms of bondage and slavery. Capturing or enslaving a fellow human being is currently an international offence condemned by all nations (Freeman 11). Unlike in the past when people were taken as slaves due to debt or as war captives, all constitutions of the world prohibit the act and even imposes heavy penalty on the offenders. Since God created human beings whe n they were not under the rule or authority of anybody, no person should be denied such right. Right to liberty ensures that people move around the earth doing as they wish with their freedom so long as they respect and accord the same to other people (Freeman 14). Therefore, people who capture their fellow human beings contradict the right to liberty. Such acts include those witnessed by illegal groups such as militants or pirates who capture sailors and keep or otherwise kill them. A good example is the capturing of sailors in Indian Ocean by Somali pirates who demand huge ransom in order to release the captives. This action is bad and illegal since it contravenes the captives’ right to move (Freeman 14). However, the right to liberty is restricted in some instances. Such instances include when a person commits a criminal offence, and thus convicted and sentenced to imprisonment. While serving a prison term, a person is denied the right to be free (Freeman 14). This is a fo rm of punishment directed at the prisoner in order to compel him to change from criminal activities. Human beings also have a right to lead a happy life on earth. Such freedom is God – given and thus, nobody should be denied (Freeman 27). God created human beings to be happy creatures and thus, should thank him for such right by worshiping him. Therefore, actions, which inhibit man from pursuing happiness on earth go against the right and thus, need condemnation. Thus, human beings have a

The importance of keeping customers for as long as possible, in what Essay

The importance of keeping customers for as long as possible, in what is seen often as a short-term approach to sales - Essay Example This is the main concern of the so-called consumer relationship management (CRM) models today. Vogt defined CRM as a customer-focused business strategy designed to optimize profitability, revenue and customer satisfaction. (p3) Shanmugasundram (2009) also posited that it is a comprehensive strategy and process of acquiring, retaining and partnering with selective customers to create superior value for the company and the customer. (p9) Unarguably there are numerous meanings and interpretations of CRM. Nonetheless, they all agree that a CRM strategy is always characterized by a quest to establish a relationship with a client in such a way that it contributes to the competitive advantage of the organization, which ultimately results to profitability. The simple logic is to make the customer happy so that the sales keep on coming. How does it work? As previously mentioned CRM strategies are diverse and varied. Sometimes, its distinction depends on the industry using it and sometimes it depends on specific needs and market trends. However, the fundamental principle emphasizes the importance given to people - naturally, there are the consumers but also, there is an emphasis given to the employees. According to Shanmugasundram, the principle is all about building enduring relationships that can lead to a profitable business organization and that both consumers and employees are fundamental to their achievement. This is expressed in the following model. Fig. 1: CRM Model (Shanmugasundram, p9) The model is pretty much straightforward: there are four crucial elements involved: Leadership, Delight, Loyalty and People. The model, as demonstrated above, puts the People at the center, with all the rest of the elements working together seamlessly for their benefit. The model ultimately aims for customer satisfaction. This is important because it has already been proven that satisfaction and a number of resulting variables, including customer loyalty lead to the probability o f purchase at different price points. This is also highlighted by the fact that only 26 percent of purchase decisions of consumers are influenced by advertising and that factors such as personal experience and referrals are more frequently cited. (Rai 2008, p145) Francis Buttle (2008) explained that, â€Å"a satisfied customer is more profitable than a dissatisfied one. If satisfaction declines, customers become more reluctant to buy unless prices are cut. If satisfaction improves the opposite is true.† (p47) This point is further reinforced by The American Customer Satisfaction Index Model (see fig. 2). Fig. 2: The American Customer Satisfaction Index Model (Buttle, p47) CRM and Technology An important variable in CRM models and strategies is the use of technology to achieve its objectives. CRM, in this context, becomes what Foss and Stone called as the methodologies, technologies and e-commerce capabilities used by companies in managing customer relationships. (p3) The adva nces in technology, particularly in communications, transportation and logistics have empowered organization to add value to their products and services. For example, an organization can use databases as part of the wider CRM strategy to build and keep accurate and up-to-date information about its customers. (Canwell and Sutherland 2003, p249) By doing so, the organization is able to analyze customer behaviors and expectations and develop better products

Biomedical Informatics Thesis Example | Topics and Well Written Essays - 8000 words

Biomedical Informatics - Thesis Example his study that it is sometimes necessary for more important information, such as a chest X-rays, physical examination, and health and occupational histories, to be made available in order to make a diagnosis. Lung diffusion capacity testing (DLCO) is a noninvasive test which is used to measure the movement of gases (most specifically oxygen) through the lung into the bloodstream. The single breath diffusing capacity test is the most common way to determine DLCO, whereby the subject is required to blow out as much air as he or she can so that only the residual gas is left in the lungs. Moreover, the subject is requested to take a deep breath in order to completely fill up the lungs. The subject is then requested to hold his or her breath for a very short period of time. The subject is then finally requested to exhale. The analysis of the gas that has been blown out will then be carried out in order to determine how much went into the bloodstream through the lungs (De Boer, 2010). 23 Pulmonary function tests (abbreviated PFT), which are also known as lung functions tests, are tests which are used so as to provide measures of gas exchange, lung volumes, flow rates, and respiratory muscle function. These tests determines the quantity of air that the lungs can hold, how fast air can possibly be moved in and out of the lungs, as well as the lungs’ ability to add oxygen and remove of carbon dioxide into and from the blood respectively. According to Goldman (2005) and De Boer (2010) the pulmonary function tests have the ability to diagnose diseases of the lungs, to measure how sever the lung problems are, and to monitor the treatment of the lung diseases. Since the spirometer was first developed in 1846 by Hutchinson, measurements of the dynamic volumes of the lung as well as of maximal flow rates have been employed to detect and quantify the diseases which affect the airways and lung parenchyma (White, 2004). The lung function may be determined by the use of tests

Leadership assessmt 2 Essay Example | Topics and Well Written Essays - 500 words

Leadership assessmt 2 - Essay Example Davis created a team to lead a centralized call center. One of the purposes of the call center was to become proactive in planning and inventory forecasting. The use of teamwork encouraged by Davis helped the company improve its organizational culture. The firm has become more flexible and adaptive which made the company more aligned with the business environment of the 21st century. Davis encouraged in state competition. He did not use competition between states to lower the risk of dysfunctional behavior occurring due to the consequences of competition. The use of competition can lead to negative behaviors such as people lying to each other and mistrust among colleagues. Competitor can also lead to people stepping on each other in order to get ahead in the corporate ladder. Another potential negative aspect of competition is that it can intensify the work conditions and environment which can lead to work related stress. Employees can become burn out due to the excessive use of competition. Competition can encourage individualism instead of cooperation. Employees that got involvement in team sports in the past are more likely to become good team players in the corporate world. Recruiters of talent look positively to a candidate having prior involvement in sporting teams. Team sports teach players important skills such as learning to cooperate with others and build bonds of trust among teammates. Sports also teach people values, discipline, and encourage good physical health. Sports teach people how to work together as a cohesive and united team. Being a good team player in the business world is an asset because cooperation and teamwork are skills that needed in corporate America. In order to implement teamwork at CCA Davis opened up the lines of communication among the workers and the managerial staff. Open communication enables the free flow of ideas. These ideas can be used by teammates to find

Biomedical Informatics Thesis Example | Topics and Well Written Essays - 8000 words

Biomedical Informatics - Thesis Example his study that it is sometimes necessary for more important information, such as a chest X-rays, physical examination, and health and occupational histories, to be made available in order to make a diagnosis. Lung diffusion capacity testing (DLCO) is a noninvasive test which is used to measure the movement of gases (most specifically oxygen) through the lung into the bloodstream. The single breath diffusing capacity test is the most common way to determine DLCO, whereby the subject is required to blow out as much air as he or she can so that only the residual gas is left in the lungs. Moreover, the subject is requested to take a deep breath in order to completely fill up the lungs. The subject is then requested to hold his or her breath for a very short period of time. The subject is then finally requested to exhale. The analysis of the gas that has been blown out will then be carried out in order to determine how much went into the bloodstream through the lungs (De Boer, 2010). 23 Pulmonary function tests (abbreviated PFT), which are also known as lung functions tests, are tests which are used so as to provide measures of gas exchange, lung volumes, flow rates, and respiratory muscle function. These tests determines the quantity of air that the lungs can hold, how fast air can possibly be moved in and out of the lungs, as well as the lungs’ ability to add oxygen and remove of carbon dioxide into and from the blood respectively. According to Goldman (2005) and De Boer (2010) the pulmonary function tests have the ability to diagnose diseases of the lungs, to measure how sever the lung problems are, and to monitor the treatment of the lung diseases. Since the spirometer was first developed in 1846 by Hutchinson, measurements of the dynamic volumes of the lung as well as of maximal flow rates have been employed to detect and quantify the diseases which affect the airways and lung parenchyma (White, 2004). The lung function may be determined by the use of tests

Nietzxche, Friedrich. On the use and abuse of History for Life Essay

Nietzxche, Friedrich. On the use and abuse of History for Life - Essay Example He was the vizier of the fourth dynasty during the reign of pharaoh Sneferu. Therefore, the writings are assumed to have been written between 2613-2589 B.C. The ancient writers did not specifically put the date of writing of the teachings but they gave the period and the king who reigned at that time. This information is used to determine age and time of the writing. The writing is categorized as wise sayings because they were written to guide the Pharaoh’s children on how they were to live and rule. According to the Egyptians, the writings are teachings which guide them in their daily ventures. The writing of these teachings took place in the Pharaoh’s palace, where their children were being taught. 2. †¦of the tree of knowledge of good and evil, thou shalt not eat of it; for in the day that thou eatest thereof thou shalt surely die. The title in which the above quote falls is creation, though others may group it as commandment. It can be argued to be creation sin ce it comes after Man and Woman have been created. On the other hand, it is said to be a commandment since it is an instruction to the created being on what they should and should not do. The commandment has more weight since it has punishment onto it that if they do not follow they will surely die. It was later found out that disregard of the instruction led to punishment and expulsion out of the garden. Thus, the quote is a command given to Adam and Eve by God in the Garden of Eden after they were created. The quote is found in the Bible, the book of Genesis chapter 2 versus seventeen. The writings were written years later by Moses despite the occurrence of the event in 4004 B.C when creation is believed to have taken place. Moses wrote the book of Genesis and grouped it with four others, naming it the book of Torah. The quote offers teaching to the believers on obedience and signifies the belief in one God to give them orders. The quote is religious due to its nature of involving beings and Supernatural forces. Moses wrote the book while in the desert with the Israelites as they were moving from Egypt into the Promised Land. 3. God created man in his own image, in the image of God created He him; male and female created He them. The quote above is extracted from creation. It justifies the existence of man as not from the natural causes but thoughtfully designed into existence by a Supernatural being. The book was written by Moses while in the desert with his fellow Israelites after running from Egypt, where they had served as slaves. The quote was used by Moses to remind the Israelites that they were created. It is extracted from the books of Moses known as the Torah, specifically the book of Genesis. The Quote was Jewish and is also used in the Christian context among those who believe in creation. The quote was said in the Garden of Eden, where the Bible states to have been the place where God began the creation. The event, therefore, occurred in 4004 B.C , the period in which it is believed the creation took place. It is found in the Bible, from the book of Genesis chapter one versus twenty seven (Genesis 1:27). The quote explains the work carried out in the sixth day of creation to crown the work that took a week; work that made the whole world. It denotes end of the creation act which was creating man. It denoted the end of the creation. The quote is categorized as religious, appreciating the existence of

University education Essay Example for Free

University education Essay Summary: This article is about the various different ideas to influence college students to attend class. Several professors feel that by using the students’ attendance and their class participation as part of their grade that more students would attend class. Studies show that students who attend class are more likely to get higher graduation rates. Some students feel that the information taught in class should also be available online, which results in students not coming to class. The researchers are saying that by the professors pleasing the students, has led to easier classes where students don’t learn as much as they used to. Ultimately, the choice is the students and the ones that show up to class are the ones getting the most information, and higher success rates. Essay: College attendance rates are going down as well as graduation rates. College students wonder why there not graduating or passing their classes. I’ll tell you why several students are failing. Students have a responsibility to show up to class to learn the information taught so they can pass, but if students don’t show up they can’t receive that information. So with students not going to class they don’t receive all the information needed to pass test, exams, and complete homework assignments. Should attendance be a part of the students’ grade? I feel that if the students’ attendance is incorporated into their grade that more students are going to get lower grades. The students are there to better themselves and if they don’t want to show up to class then they are only hurting themselves. The students know that if they don’t attend class then they won’t receive all the information to do well and pass the class. I agree with researcher Marcus Crede that mandatory attendance at the college level is the wrong approach. Professors should influence their students to come to class in other ways than using attendance as part of their grade. The easiest solution to this problem is for the students to attend class. All college students shouldn’t have to be told this because they know what is expected of them and what they have to do to pass. Graduation rates should increase as long as the students attend class and pay attention. Students’ attendance shouldn’t have to be a percentage of their grade, but if that’s what it takes to increase graduation rates then that’s what will have to happen. So if you want to be a successful college student you need to attend class, pay attention, and study for your test and exams. Reflection: I feel that I did a good job explaining the key points of the article in my own words. Also I think that I put all the key points in the correct paragraphs. My weaknesses are repeating myself in different words. Also not explaining my thoughts clearly and easily understood. I used too many of my own thoughts without any examples from the text to explain my thoughts.

Lost Love Essay -- essays research papers

My Lost Love It was ever so dark that evening. It hurt to look at her. It was like looking at my heart barely beating on the floor. I couldn't stand it. Love never hurt me this much. I can't believe this happened. Why me? Why her? Why us? In an instant it was over. I remember the first time we met. It was actually kind of funny. She was walking her dog. Actually, the dog was walking her. I was reading a poem and walking along. When all of the sudden, we collided. The second I looked up into her eyes, I fell for her harder than an avalanche off of Mount Everest. I think she felt the same way because we didn't look away for what seemed like 5 hours. We talked in the park for about an hour and a half. She told me her name was Kristeen Thorne and told me that she was a new student at Orangeville High School; the same school I attended. We found that we had six out of seven classes together, which was a very good thing. I asked her to go out with me that Friday night. We went to the movies first, and then we went to Vinnie Vicci's Italian restaurant. The date was perfect and the person I was with made the date seem like Heaven. We dated non- exclusively for about one month. On our one month anniversary, I gave her my letter jacket which I earned playing varsity football. And while I did that, I asked her to date exclusively. She answered my question so fast I didn't realize that she said yes. We started going out together almost every weekend and talked on the phone all night and walked with each other to class everyday, and I gave her a ride to and from school everyday. We had been going out for about 3 months. The student body voted us cutest couple of the year. We had to get our pictures taken for the year book. We went to the spring dance together and were voted the king and queen of the dance. Then the school year came to an end. That summer we spent most of the time together. We went to Kyber Lake for the Fourth of July weekend. My dad let us borrow his boat for the weeken d, and we stayed at a camp ground. On Friday, when we got there, I took the boat for a test drive to see if it was still running. It's was working. I took her to the little secret cove that only I knew. We stayed in that cove for about 2 hours just talking and kissing and gazing at each other. At that time, the only thing I was hoping for was that this moment never would end. When I look... ...d all he can do is stare. Tell my sister not to cry. Tell Dad to be brave. And when I go to heaven, put "Daddy's Girl" on my grave. Someone should have told him, not to drink and drive. If only they had told him, I would still be alive. My breath is getting shorter. I'm becoming very scared. Please don't cry for me. When I needed you, you were always there. I have one last question, before I say good bye. I didn't drink and drive, so why am I the one to die?" " I know I have to get on with my life, but I will always love and cherish you. Our love will always be like the stars eternal shine." I said as I wiped the tears from my face. " One more thing before I go, I love you! Remember that!" I looked at her grave imagining her face. I stood there for a couple of minutes not saying a word. Then I turned and began to walk to my car. When I got into the car, I sat there, remembering, one at a time, all the things we did together. The final thing I sa w was the twinkle in her eye and the smile on her face when she promised me that she would never leave me. Then I drove home knowing, I would never get to kiss her sweet, gentle lips good night ever again.

The Role of Estrogen in Sexual Differentiation :: Biology Essays Research Papers

The Role of Estrogen in Sexual Differentiation Most, if not all, species with two sexes exhibit sexually dimorphic behavior and physical characteristics. These dimorphisms can be attributed to differences in the brain, such as size or function of structure, and these brain structures can be affected by the hormones circulated throughout the organism. It has been held that the sexual dimorphisms rely only on the presence or absence of androgen, namely, testosterone, during the critical period of development for an organism; however, new research suggests that the presence of estrogen, specifically estradiol, has an active role in sexual differentiation. Several sexual dimorphic structures in the brain have been observed in laboratory experiments. The corpus callosum in male rats is much larger than that in female rats, and this size difference is uncorrelated with total brain weight. These findings led many to investigate the relationship between human male and female corpus callosa. A paper published by de Lacoste-Utamsing and Holloway stated that the splenium of the callosum is larger in women than in men, but their finding has since been challenged by several reports stating that there exists no sexual dimorphism. Analysis done from 1982-1994 reveals a small difference of corpus callosum size in favor of males, but it is hypothesized that age, handedness, overall brain size and weight, and incorrect statistics were not taken into account. (3) There has also been controversy in the research involving the brain region INAH-3 in humans. The heterosexual male INAH-3 is larger than that of heterosexual females; the INAH-3 in homosexual males is on the average smaller than that of heterosexual males and approximately the same size of heterosexual females. The general population has attempted to use this fact as an explanation of the biological basis of homosexuality, though the differences in structure may not be causally related to the sexual orientation of the man. Because we can only observe behaviors when doing experiments with lab animals, the data cannot firmly establish a basis for sexual orientation. The traditional view on sexual differentiation is that organizational effects from hormones which occur during neonatal development are the master plan for the organisms sex and corresponding behaviors and characteristics. Exposure to androgen, namely, testosterone, would result in a male organism, while exposure to neither androgen nor estrogen would result in the default sex: female. Characteristics resulting from organizational effects include formation of genitalia and traits such as aggression. The Role of Estrogen in Sexual Differentiation :: Biology Essays Research Papers The Role of Estrogen in Sexual Differentiation Most, if not all, species with two sexes exhibit sexually dimorphic behavior and physical characteristics. These dimorphisms can be attributed to differences in the brain, such as size or function of structure, and these brain structures can be affected by the hormones circulated throughout the organism. It has been held that the sexual dimorphisms rely only on the presence or absence of androgen, namely, testosterone, during the critical period of development for an organism; however, new research suggests that the presence of estrogen, specifically estradiol, has an active role in sexual differentiation. Several sexual dimorphic structures in the brain have been observed in laboratory experiments. The corpus callosum in male rats is much larger than that in female rats, and this size difference is uncorrelated with total brain weight. These findings led many to investigate the relationship between human male and female corpus callosa. A paper published by de Lacoste-Utamsing and Holloway stated that the splenium of the callosum is larger in women than in men, but their finding has since been challenged by several reports stating that there exists no sexual dimorphism. Analysis done from 1982-1994 reveals a small difference of corpus callosum size in favor of males, but it is hypothesized that age, handedness, overall brain size and weight, and incorrect statistics were not taken into account. (3) There has also been controversy in the research involving the brain region INAH-3 in humans. The heterosexual male INAH-3 is larger than that of heterosexual females; the INAH-3 in homosexual males is on the average smaller than that of heterosexual males and approximately the same size of heterosexual females. The general population has attempted to use this fact as an explanation of the biological basis of homosexuality, though the differences in structure may not be causally related to the sexual orientation of the man. Because we can only observe behaviors when doing experiments with lab animals, the data cannot firmly establish a basis for sexual orientation. The traditional view on sexual differentiation is that organizational effects from hormones which occur during neonatal development are the master plan for the organisms sex and corresponding behaviors and characteristics. Exposure to androgen, namely, testosterone, would result in a male organism, while exposure to neither androgen nor estrogen would result in the default sex: female. Characteristics resulting from organizational effects include formation of genitalia and traits such as aggression.

Statistics for Business and Economics

Openmirrors. com CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION Cumulative probability Entries in this table give the area under the curve to the left of the z value. For example, for z = –. 85, the cumulative probability is . 1977. z 0 z 3. 0 2. 9 2. 8 2. 7 2. 6 2. 5 2. 4 2. 3 2. 2 2. 1 2. 0 1. 9 1. 8 1. 7 1. 6 1. 5 1. 4 1. 3 1. 2 1. 1 1. 0 . 9 . 8 . 7 . 6 . 5 . 4 . 3 . 2 . 1 . 0 .00 . 0013 . 0019 . 0026 . 0035 . 0047 . 0062 . 0082 . 0107 . 0139 . 0179 . 0228 . 0287 . 0359 . 0446 . 0548 . 0668 . 0808 . 0968 . 1151 . 1357 . 1587 . 1841 . 2119 . 2420 . 2743 . 3085 . 3446 . 3821 . 4207 . 4602 . 5000 01 . 0013 . 0018 . 0025 . 0034 . 0045 . 0060 . 0080 . 0104 . 0136 . 0174 . 0222 . 0281 . 0351 . 0436 . 0537 . 0655 . 0793 . 0951 . 1131 . 1335 . 1562 . 1814 . 2090 . 2389 . 2709 . 3050 . 3409 . 3783 . 4168 . 4562 . 4960 .02 . 0013 . 0018 . 0024 . 0033 . 0044 . 0059 . 0078 . 0102 . 0132 . 0170 . 0217 . 0274 . 0344 . 0427 . 0526 . 0643 . 0778 . 0934 . 1112 . 1314 . 1539 . 1788 . 2061 . 2358 . 2676 . 3015 . 3372 . 3745 . 4129 . 4522 . 4920 .03 . 0012 . 0017 . 0023 . 0032 . 0043 . 0057 . 0075 . 0099 . 0129 . 0166 . 0212 . 0268 . 0336 . 0418 . 0516 . 0630 . 0764 . 0918 . 1093 . 1292 . 1515 . 1762 . 2033 . 2327 . 643 . 2981 . 3336 . 3707 . 4090 . 4483 . 4880 .04 . 0012 . 0016 . 0023 . 0031 . 0041 . 0055 . 0073 . 0096 . 0125 . 0162 . 0207 . 0262 . 0329 . 0409 . 0505 . 0618 . 0749 . 0901 . 1075 . 1271 . 1492 . 1736 . 2005 . 2296 . 2611 . 2946 . 3300 . 3669 . 4052 . 4443 . 4840 .05 . 0011 . 0016 . 0022 . 0030 . 0040 . 0054 . 0071 . 0094 . 0122 . 0158 . 0202 . 0256 . 0322 . 0401 . 0495 . 0606 . 0735 . 0885 . 1056 . 1251 . 1469 . 1711 . 1977 . 2266 . 2578 . 2912 . 3264 . 3632 . 4013 . 4404 . 4801 .06 . 0011 . 0015 . 0021 . 0029 . 0039 . 0052 . 0069 . 0091 . 0119 . 0154 . 0197 . 0250 . 0314 . 0392 . 0485 . 0594 . 0721 . 0869 . 038 . 1230 . 1446 . 1685 . 1949 . 2236 . 2546 . 2877 . 3228 . 3594 . 3974 . 4364 . 4761 .07 . 0011 . 0015 . 0021 . 0028 . 0038 . 0051 . 0068 . 0089 . 0116 . 0150 . 0192 . 0244 . 0307 . 0384 . 0475 . 0582 . 0708 . 0853 . 1020 . 1210 . 1423 . 1660 . 1922 . 2206 . 2514 . 2843 . 3192 . 3557 . 3936 . 4325 . 4721 .08 . 0010 . 0014 . 0020 . 0027 . 0037 . 0049 . 0066 . 0087 . 0113 . 0146 . 0188 . 0239 . 0301 . 0375 . 0465 . 0571 . 0694 . 0838 . 1003 . 1190 . 1401 . 1635 . 1894 . 2177 . 2483 . 2810 . 3156 . 3520 . 3897 . 4286 . 4681 .09 . 0010 . 0014 . 0019 . 0026 . 0036 . 0048 . 0064 . 0084 . 0110 . 0143 . 0183 . 0233 . 294 . 0367 . 0455 . 0559 . 0681 . 0823 . 0985 . 1170 . 1379 . 1611 . 1867 . 2148 . 2451 . 2776 . 3121 . 3483 . 3859 . 4247 . 4641 CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION Cumulative probability Entries in the table give the area under the curve to the left of the z value. For example, for z = 1. 25, the cumulative probability is . 8944. 0 z z . 0 . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 1. 0 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 1. 7 1. 8 1. 9 2. 0 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 2. 9 3. 0 .00 . 5000 . 5398 . 5793 . 6179 . 6554 . 6915 . 7257 . 7580 . 7881 . 8159 . 8413 . 8643 . 8849 . 9032 . 192 . 9332 . 9452 . 9554 . 9641 . 9713 . 9772 . 9821 . 9861 . 9893 . 9918 . 9938 . 9953 . 9965 . 9974 . 9981 . 9987 .01 . 5040 . 5438 . 5832 . 6217 . 6591 . 6950 . 7291 . 7611 . 7910 . 8186 . 8438 . 8665 . 8869 . 9049 . 9207 . 9345 . 9463 . 9564 . 9649 . 9719 . 9778 . 9826 . 9864 . 9896 . 9920 . 9940 . 9955 . 9966 . 9975 . 9982 . 9987 .02 . 5080 . 5478 . 5871 . 6255 . 6628 . 6985 . 7324 . 7642 . 7939 . 8212 . 8461 . 8686 . 8888 . 9066 . 9222 . 9357 . 9474 . 9573 . 9656 . 9726 . 9783 . 9830 . 9868 . 9898 . 9922 . 9941 . 9956 . 9967 . 9976 . 9982 . 9987 .03 . 5120 . 5517 . 5910 . 6293 . 6664 . 7019 . 7357 . 7673 . 967 . 8238 . 8485 . 8708 . 8907 . 9082 . 9236 . 9370 . 9484 . 9582 . 9664 . 9732 . 9788 . 9834 . 9871 . 9901 . 9925 . 9943 . 9957 . 9968 . 9977 . 9983 . 9988 .04 . 5160 . 5557 . 5948 . 6331 . 6700 . 7054 . 7389 . 7704 . 7995 . 8264 . 8508 . 8729 . 8925 . 9099 . 9251 . 938 2 . 9495 . 9591 . 9671 . 9738 . 9793 . 9838 . 9875 . 9904 . 9927 . 9945 . 9959 . 9969 . 9977 . 9984 . 9988 .05 . 5199 . 5596 . 5987 . 6368 . 6736 . 7088 . 7422 . 7734 . 8023 . 8289 . 8531 . 8749 . 8944 . 9115 . 9265 . 9394 . 9505 . 9599 . 9678 . 9744 . 9798 . 9842 . 9878 . 9906 . 9929 . 9946 . 9960 . 9970 . 9978 . 9984 . 9989 .06 . 5239 . 636 . 6026 . 6406 . 6772 . 7123 . 7454 . 7764 . 8051 . 8315 . 8554 . 8770 . 8962 . 9131 . 9279 . 9406 . 9515 . 9608 . 9686 . 9750 . 9803 . 9846 . 9881 . 9909 . 9931 . 9948 . 9961 . 9971 . 9979 . 9985 . 9989 .07 . 5279 . 5675 . 6064 . 6443 . 6808 . 7157 . 7486 . 7794 . 8078 . 8340 . 8577 . 8790 . 8980 . 9147 . 9292 . 9418 . 9525 . 9616 . 9693 . 9756 . 9808 . 9850 . 9884 . 9911 . 9932 . 9949 . 9962 . 9972 . 9979 . 9985 . 9989 .08 . 5319 . 5714 . 6103 . 6480 . 6844 . 7190 . 7517 . 7823 . 8106 . 8365 . 8599 . 8810 . 8997 . 9162 . 9306 . 9429 . 9535 . 9625 . 9699 . 9761 . 9812 . 9854 . 9887 . 9913 . 9934 . 9951 . 963 . 9973 . 9980 . 9986 . 9990 .09 . 53 59 . 5753 . 6141 . 6517 . 6879 . 7224 . 7549 . 7852 . 8133 . 8389 . 8621 . 8830 . 9015 . 9177 . 9319 . 9441 . 9545 . 9633 . 9706 . 9767 . 9817 . 9857 . 9890 . 9916 . 9936 . 9952 . 9964 . 9974 . 9981 . 9986 . 9990 STATISTICS FOR BUSINESS AND ECONOMICS 11e This page intentionally left blank STATISTICS FOR BUSINESS AND ECONOMICS 11e David R. Anderson University of Cincinnati Dennis J. Sweeney University of Cincinnati Thomas A. Williams Rochester Institute of Technology Statistics for Business and Economics, Eleventh Edition David R. Anderson, Dennis J. Sweeney, Thomas A.Williams VP/Editorial Director: Jack W. Calhoun Publisher: Joe Sabatino Senior Acquisitions Editor: Charles McCormick, Jr. Developmental Editor: Maggie Kubale Editorial Assistant: Nora Heink Marketing Communications Manager: Libby Shipp Content Project Manager: Jacquelyn K Featherly Media Editor: Chris Valentine Manufacturing Coordinator: Miranda Kipper Production House/Compositor: MPS Limited, A Macmillan Company Senio r Art Director: Stacy Jenkins Shirley Internal Designer: Michael Stratton/cmiller design Cover Designer: Craig Ramsdell Cover Images: Getty Images/GlowImages Photography Manager: John Hill 2011, 2008 South-Western, Cengage Learning ALL RIGHTS RESERVED. No part of this work covered by the copyright herein may be reproduced, transmitted, stored or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, information networks, or information storage and retrieval systems, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the publisher.For product information and technology assistance, contact us at Cengage Learning Customer & Sales Support, 1-800-354-9706 For permission to use material from this text or product, submit all requests online at cengage. com/permissions Further permissions questions can be emailed to [email  protected] com ExamView  ® is a registered trademark of eInstruction Corp. Windows is a registered trademark of the Microsoft Corporation used herein under license.Macintosh and Power Macintosh are registered trademarks of Apple Computer, Inc. used herein under license. Library of Congress Control Number: 2009932190 Student Edition ISBN 13: 978-0-324-78325-4 Student Edition ISBN 10: 0-324-78325-6 Instructor's Edition ISBN 13: 978-0-538-45149-9 Instructor's Edition ISBN 10: 0-538-45149-1 South-Western Cengage Learning 5191 Natorp Boulevard Mason, OH 45040 USA Cengage Learning products are represented in Canada by Nelson Education, Ltd.For your course and learning solutions, visit www. cengage. com Purchase any of our products at your local college store or at our preferred online store www. ichapters. com Printed in the United States of America 1 2 3 4 5 6 7 13 12 11 10 09 Dedicated to Marcia, Cherri, and Robbie This page intentionally left blank Brief Conte ntsPreface xxv About the Authors xxix Chapter 1 Data and Statistics 1 Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations 31 Chapter 3 Descriptive Statistics: Numerical Measures 85 Chapter 4 Introduction to Probability 148 Chapter 5 Discrete Probability Distributions 193 Chapter 6 Continuous Probability Distributions 232 Chapter 7 Sampling and Sampling Distributions 265 Chapter 8 Interval Estimation 308 Chapter 9 Hypothesis Tests 348 Chapter 10 Inference About Means and Proportions with Two Populations 406 Chapter 11 Inferences About Population Variances 448 Chapter 12 Tests of Goodness of Fit and Independence 472 Chapter 13 Experimental Design and Analysis of Variance 506 Chapter 14 Simple Linear Regression 560 Chapter 15 Multiple Regression 642 Chapter 16 Regression Analysis: ModelBuilding 712 Chapter 17 Index Numbers 763 Chapter 18 Time Series Analysis and Forecasting 784 Chapter 19 Nonparametric Methods 855 Chapter 20 Statistical Methods for Quality Control 903 Chapter 21 Decision Analysis 937 Chapter 22 Sample Survey On Website Appendix A References and Bibliography 976 Appendix B Tables 978 Appendix C Summation Notation 1005 Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 1007 Appendix E Using Excel Functions 1062 Appendix F Computing p-Values Using Minitab and Excel 1067 Index 1071 This page intentionally left blank Contents Preface xxv About the Authors xxix Chapter 1 Data and Statistics 1 Statistics in Practice: BusinessWeek 2 1. 1 Applications in Business and Economics 3 Accounting 3 Finance 4 Marketing 4 Production 4 Economics 4 1. Data 5 Elements, Variables, and Observations 5 Scales of Measurement 6 Categorical and Quantitative Data 7 Cross-Sectional and Time Series Data 7 1. 3 Data Sources 10 Existing Sources 10 Statistical Studies 11 Data Acquisition Errors 13 1. 4 Descriptive Statistics 13 1. 5 Statistical Inference 15 1. 6 Computers and Statistical Analysis 17 1. 7 Data Mining 17 1. 8 Ethical Guidelines for Statistical Practice 18 Summary 20 Glossary 20 Supplementary Exercises 21 Appendix: An Introduction to StatTools 28 Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations 31 Statistics in Practice: Colgate-Palmolive Company 32 2. 1 Summarizing Categorical Data 33 Frequency Distribution 33 Relative Frequency and Percent Frequency Distributions 34 Bar Charts and Pie Charts 34 x Contents 2. Summarizing Quantitative Data 39 Frequency Distribution 39 Relative Frequency and Percent Frequency Distributions 41 Dot Plot 41 Histogram 41 Cumulative Distributions 43 Ogive 44 2. 3 Exploratory Data Analysis: The Stem-and-Leaf Display 48 2. 4 Crosstabulations and Scatter Diagrams 53 Crosstabulation 53 Simpson’s Paradox 56 Scatter Diagram and Trendline 57 Summary 63 Glossary 64 Key Formulas 65 Supplementary Exercises 65 Case Problem 1: Pelican Stores 71 Case Problem 2: Motion Picture Industry 72 Appendix 2. 1 Using Minitab for Tabular and Graphical Presentations 73 Appendi x 2. 2 Using Excel for Tabular and Graphical Presentations 75 Appendix 2. 3 Using StatTools for Tabular and Graphical Presentations 84 Chapter 3 Descriptive Statistics: Numerical Measures 85 Statistics in Practice: Small Fry Design 86 3. Measures of Location 87 Mean 87 Median 88 Mode 89 Percentiles 90 Quartiles 91 3. 2 Measures of Variability 95 Range 96 Interquartile Range 96 Variance 97 Standard Deviation 99 Coefficient of Variation 99 3. 3 Measures of Distribution Shape, Relative Location, and Detecting Outliers 102 Distribution Shape 102 z-Scores 103 Chebyshev’s Theorem 104 Empirical Rule 105 Detecting Outliers 106 Contents xi 3. 4 Exploratory Data Analysis 109 Five-Number Summary 109 Box Plot 110 3. 5 Measures of Association Between Two Variables 115 Covariance 115 Interpretation of the Covariance 117 Correlation Coefficient 119 Interpretation of the Correlation Coefficient 120 3. The Weighted Mean and Working with Grouped Data 124 Weighted Mean 124 Grouped Data 125 Summ ary 129 Glossary 130 Key Formulas 131 Supplementary Exercises 133 Case Problem 1: Pelican Stores 137 Case Problem 2: Motion Picture Industry 138 Case Problem 3: Business Schools of Asia-Pacific 139 Case Problem 4: Heavenly Chocolates Website Transactions 139 Appendix 3. 1 Descriptive Statistics Using Minitab 142 Appendix 3. 2 Descriptive Statistics Using Excel 143 Appendix 3. 3 Descriptive Statistics Using StatTools 146 Chapter 4 Introduction to Probability 148 Statistics in Practice: Oceanwide Seafood 149 4. 1 Experiments, Counting Rules, and Assigning Probabilities 150 Counting Rules, Combinations, and Permutations 151 Assigning Probabilities 155 Probabilities for the KP&L Project 157 4. 2 Events and Their Probabilities 160 4. 3 Some Basic Relationships of Probability 164 Complement of an Event 164 Addition Law 165 4. 4 Conditional Probability 171 Independent Events 174 Multiplication Law 174 4. Bayes’ Theorem 178 Tabular Approach 182 Summary 184 Glossary 184 xii Contents K ey Formulas 185 Supplementary Exercises 186 Case Problem: Hamilton County Judges 190 Chapter 5 Discrete Probability Distributions 193 Statistics in Practice: Citibank 194 5. 1 Random Variables 194 Discrete Random Variables 195 Continuous Random Variables 196 5. 2 Discrete Probability Distributions 197 5. 3 Expected Value and Variance 202 Expected Value 202 Variance 203 5. 4 Binomial Probability Distribution 207 A Binomial Experiment 208 Martin Clothing Store Problem 209 Using Tables of Binomial Probabilities 213 Expected Value and Variance for the Binomial Distribution 214 5. Poisson Probability Distribution 218 An Example Involving Time Intervals 218 An Example Involving Length or Distance Intervals 220 5. 6 Hypergeometric Probability Distribution 221 Summary 225 Glossary 225 Key Formulas 226 Supplementary Exercises 227 Appendix 5. 1 Discrete Probability Distributions with Minitab 230 Appendix 5. 2 Discrete Probability Distributions with Excel 230 Chapter 6 Continuous Probability D istributions 232 Statistics in Practice: Procter & Gamble 233 6. 1 Uniform Probability Distribution 234 Area as a Measure of Probability 235 6. 2 Normal Probability Distribution 238 Normal Curve 238 Standard Normal Probability Distribution 40 Computing Probabilities for Any Normal Probability Distribution 245 Grear Tire Company Problem 246 6. 3 Normal Approximation of Binomial Probabilities 250 6. 4 Exponential Probability Distribution 253 Computing Probabilities for the Exponential Distribution 254 Relationship Between the Poisson and Exponential Distributions 255 Contents xiii Summary 257 Glossary 258 Key Formulas 258 Supplementary Exercises 258 Case Problem: Specialty Toys 261 Appendix 6. 1 Continuous Probability Distributions with Minitab 262 Appendix 6. 2 Continuous Probability Distributions with Excel 263 Chapter 7 Sampling and Sampling Distributions 265 Statistics in Practice: MeadWestvaco Corporation 266 7. 1 The Electronics Associates Sampling Problem 267 7. Selecting a Sam ple 268 Sampling from a Finite Population 268 Sampling from an Infinite Population 270 7. 3 Point Estimation 273 Practical Advice 275 7. 4 Introduction to Sampling Distributions 276 _ 7. 5 Sampling Distribution of x 278 _ Expected Value of x 279 _ Standard Deviation of x 280 _ Form of the Sampling Distribution of x 281 _ Sampling Distribution of x for the EAI Problem 283 _ Practical Value of the Sampling Distribution of x 283 Relationship Between the Sample Size and the Sampling _ Distribution of x 285 _ 7. 6 Sampling Distribution of p 289 _ Expected Value of p 289 _ Standard Deviation of p 290 _ Form of the Sampling Distribution of p 291 _ Practical Value of the Sampling Distribution of p 291 7. Properties of Point Estimators 295 Unbiased 295 Efficiency 296 Consistency 297 7. 8 Other Sampling Methods 297 Stratified Random Sampling 297 Cluster Sampling 298 Systematic Sampling 298 Convenience Sampling 299 Judgment Sampling 299 Summary 300 Glossary 300 Key Formulas 301 xiv Contents Su pplementary Exercises 302 _ Appendix 7. 1 The Expected Value and Standard Deviation of x 304 Appendix 7. 2 Random Sampling with Minitab 306 Appendix 7. 3 Random Sampling with Excel 306 Appendix 7. 4 Random Sampling with StatTools 307 Chapter 8 Interval Estimation 308 Statistics in Practice: Food Lion 309 8. 1 Population Mean: Known 310 Margin of Error and the Interval Estimate 310 Practical Advice 314 8. Population Mean: Unknown 316 Margin of Error and the Interval Estimate 317 Practical Advice 320 Using a Small Sample 320 Summary of Interval Estimation Procedures 322 8. 3 Determining the Sample Size 325 8. 4 Population Proportion 328 Determining the Sample Size 330 Summary 333 Glossary 334 Key Formulas 335 Supplementary Exercises 335 Case Problem 1: Young Professional Magazine 338 Case Problem 2: Gulf Real Estate Properties 339 Case Problem 3: Metropolitan Research, Inc. 341 Appendix 8. 1 Interval Estimation with Minitab 341 Appendix 8. 2 Interval Estimation with Excel 343 Appendix 8. 3 Interval Estimation with StatTools 346 Chapter 9 Hypothesis Tests 348 Statistics in Practice: John Morrell & Company 349 9. Developing Null and Alternative Hypotheses 350 The Alternative Hypothesis as a Research Hypothesis 350 The Null Hypothesis as an Assumption to Be Challenged 351 Summary of Forms for Null and Alternative Hypotheses 352 9. 2 Type I and Type II Errors 353 9. 3 Population Mean: Known 356 One-Tailed Test 356 Two-Tailed Test 362 Summary and Practical Advice 365 Contents xv Relationship Between Interval Estimation and Hypothesis Testing 366 9. 4 Population Mean: Unknown 370 One-Tailed Test 371 Two-Tailed Test 372 Summary and Practical Advice 373 9. 5 Population Proportion 376 Summary 379 9. 6 Hypothesis Testing and Decision Making 381 9. 7 Calculating the Probability of Type II Errors 382 9. Determining the Sample Size for a Hypothesis Test About a Population Mean 387 Summary 391 Glossary 392 Key Formulas 392 Supplementary Exercises 393 Case Problem 1: Quality A ssociates, Inc. 396 Case Problem 2: Ethical Behavior of Business Students at Bayview University 397 Appendix 9. 1 Hypothesis Testing with Minitab 398 Appendix 9. 2 Hypothesis Testing with Excel 400 Appendix 9. 3 Hypothesis Testing with StatTools 404 Chapter 10 Inference About Means and Proportions with Two Populations 406 Statistics in Practice: U. S. Food and Drug Administration 407 10. 1 Inferences About the Difference Between Two Population Means: 1 and 2 Known 408 Interval Estimation of 1 – 2 408 Hypothesis Tests About 1 – 2 410 Practical Advice 412 10. Inferences About the Difference Between Two Population Means: 1 and 2 Unknown 415 Interval Estimation of 1 – 2 415 Hypothesis Tests About 1 – 2 417 Practical Advice 419 10. 3 Inferences About the Difference Between Two Population Means: Matched Samples 423 10. 4 Inferences About the Difference Between Two Population Proportions 429 Interval Estimation of p1 – p2 429 Hypothesis Tests About p1 â⠂¬â€œ p2 431 Summary 436 xvi Contents Glossary 436 Key Formulas 437 Supplementary Exercises 438 Case Problem: Par, Inc. 441 Appendix 10. 1 Inferences About Two Populations Using Minitab 442 Appendix 10. 2 Inferences About Two Populations Using Excel 444 Appendix 10. Inferences About Two Populations Using StatTools 446 Chapter 11 Inferences About Population Variances 448 Statistics in Practice: U. S. Government Accountability Office 449 11. 1 Inferences About a Population Variance 450 Interval Estimation 450 Hypothesis Testing 454 11. 2 Inferences About Two Population Variances 460 Summary 466 Key Formulas 467 Supplementary Exercises 467 Case Problem: Air Force Training Program 469 Appendix 11. 1 Population Variances with Minitab 470 Appendix 11. 2 Population Variances with Excel 470 Appendix 11. 3 Population Standard Deviation with StatTools 471 Chapter 12 Tests of Goodness of Fit and Independence 472 Statistics in Practice: United Way 473 12. Goodness of Fit Test: A Multinomial Pop ulation 474 12. 2 Test of Independence 479 12. 3 Goodness of Fit Test: Poisson and Normal Distributions 487 Poisson Distribution 487 Normal Distribution 491 Summary 496 Glossary 497 Key Formulas 497 Supplementary Exercises 497 Case Problem: A Bipartisan Agenda for Change 501 Appendix 12. 1 Tests of Goodness of Fit and Independence Using Minitab 502 Appendix 12. 2 Tests of Goodness of Fit and Independence Using Excel 503 Chapter 13 Experimental Design and Analysis of Variance 506 Statistics in Practice: Burke Marketing Services, Inc. 507 13. 1 An Introduction to Experimental Design and Analysis of Variance 508 Contents xviiData Collection 509 Assumptions for Analysis of Variance 510 Analysis of Variance: A Conceptual Overview 510 13. 2 Analysis of Variance and the Completely Randomized Design 513 Between-Treatments Estimate of Population Variance 514 Within-Treatments Estimate of Population Variance 515 Comparing the Variance Estimates: The F Test 516 ANOVA Table 518 Computer Results for Analysis of Variance 519 Testing for the Equality of k Population Means:An Observational Study 520 13. 3 Multiple Comparison Procedures 524 Fisher’s LSD 524 Type I Error Rates 527 13. 4 Randomized Block Design 530 Air Traffic Controller Stress Test 531 ANOVA Procedure 532 Computations and Conclusions 533 13. Factorial Experiment 537 ANOVA Procedure 539 Computations and Conclusions 539 Summary 544 Glossary 545 Key Formulas 545 Supplementary Exercises 547 Case Problem 1: Wentworth Medical Center 552 Case Problem 2: Compensation for Sales Professionals 553 Appendix 13. 1 Analysis of Variance with Minitab 554 Appendix 13. 2 Analysis of Variance with Excel 555 Appendix 13. 3 Analysis of Variance with StatTools 557 Chapter 14 Simple Linear Regression 560 Statistics in Practice: Alliance Data Systems 561 14. 1 Simple Linear Regression Model 562 Regression Model and Regression Equation 562 Estimated Regression Equation 563 14. 2 Least Squares Method 565 14. Coefficient of Determ ination 576 Correlation Coefficient 579 14. 4 Model Assumptions 583 14. 5 Testing for Significance 585 Estimate of 2 585 t Test 586 xviii Contents Confidence Interval for 1 587 F Test 588 Some Cautions About the Interpretation of Significance Tests 590 14. 6 Using the Estimated Regression Equation for Estimation and Prediction 594 Point Estimation 594 Interval Estimation 594 Confidence Interval for the Mean Value of y 595 Prediction Interval for an Individual Value of y 596 14. 7 Computer Solution 600 14. 8 Residual Analysis: Validating Model Assumptions 605 Residual Plot Against x 606 Residual Plot Against y 607 ? Standardized Residuals 607 Normal Probability Plot 610 14. Residual Analysis: Outliers and Influential Observations 614 Detecting Outliers 614 Detecting Influential Observations 616 Summary 621 Glossary 622 Key Formulas 623 Supplementary Exercises 625 Case Problem 1: Measuring Stock Market Risk 631 Case Problem 2: U. S. Department of Transportation 632 Case Problem 3: Alu mni Giving 633 Case Problem 4: PGA Tour Statistics 633 Appendix 14. 1 Calculus-Based Derivation of Least Squares Formulas 635 Appendix 14. 2 A Test for Significance Using Correlation 636 Appendix 14. 3 Regression Analysis with Minitab 637 Appendix 14. 4 Regression Analysis with Excel 638 Appendix 14. 5 Regression Analysis with StatTools 640 Chapter 15 Multiple Regression 642 Statistics in Practice: dunnhumby 643 15. 1 Multiple Regression Model 644 Regression Model and Regression Equation 644 Estimated Multiple Regression Equation 644 15. Least Squares Method 645 An Example: Butler Trucking Company 646 Note on Interpretation of Coefficients 648 15. 3 Multiple Coefficient of Determination 654 15. 4 Model Assumptions 657 Contents xix 15. 5 Testing for Significance 658 F Test 658 t Test 661 Multicollinearity 662 15. 6 Using the Estimated Regression Equation for Estimation and Prediction 665 15. 7 Categorical Independent Variables 668 An Example: Johnson Filtration, Inc. 668 Interpreting the Parameters 670 More Complex Categorical Variables 672 15. 8 Residual Analysis 676 Detecting Outliers 678 Studentized Deleted Residuals and Outliers 678 Influential Observations 679 Using Cook’s Distance Measure to Identify Influential Observations 679 15. Logistic Regression 683 Logistic Regression Equation 684 Estimating the Logistic Regression Equation 685 Testing for Significance 687 Managerial Use 688 Interpreting the Logistic Regression Equation 688 Logit Transformation 691 Summary 694 Glossary 695 Key Formulas 696 Supplementary Exercises 698 Case Problem 1: Consumer Research, Inc. 704 Case Problem 2: Alumni Giving 705 Case Problem 3: PGA Tour Statistics 705 Case Problem 4: Predicting Winning Percentage for the NFL 708 Appendix 15. 1 Multiple Regression with Minitab 708 Appendix 15. 2 Multiple Regression with Excel 709 Appendix 15. 3 Logistic Regression with Minitab 710 Appendix 15. 4 Multiple Regression with StatTools 711Chapter 16 Regression Analysis: Model Buildi ng 712 Statistics in Practice: Monsanto Company 713 16. 1 General Linear Model 714 Modeling Curvilinear Relationships 714 Interaction 718 xx Contents Transformations Involving the Dependent Variable 720 Nonlinear Models That Are Intrinsically Linear 724 16. 2 Determining When to Add or Delete Variables 729 General Case 730 Use of p-Values 732 16. 3 Analysis of a Larger Problem 735 16. 4 Variable Selection Procedures 739 Stepwise Regression 739 Forward Selection 740 Backward Elimination 741 Best-Subsets Regression 741 Making the Final Choice 742 16. 5 Multiple Regression Approach to Experimental Design 745 16. Autocorrelation and the Durbin-Watson Test 750 Summary 754 Glossary 754 Key Formulas 754 Supplementary Exercises 755 Case Problem 1: Analysis of PGA Tour Statistics 758 Case Problem 2: Fuel Economy for Cars 759 Appendix 16. 1 Variable Selection Procedures with Minitab 760 Appendix 16. 2 Variable Selection Procedures with StatTools 761 Chapter 17 Index Numbers 763 Statistics in Practice: U. S. Department of Labor, Bureau of Labor Statistics 764 17. 1 Price Relatives 765 17. 2 Aggregate Price Indexes 765 17. 3 Computing an Aggregate Price Index from Price Relatives 769 17. 4 Some Important Price Indexes 771 Consumer Price Index 771 Producer Price Index 771 Dow Jones Averages 772 17. 5 Deflating a Series by Price Indexes 773 17. 6 Price Indexes: Other Considerations 777 Selection of Items 777 Selection of a Base Period 777 Quality Changes 777 17. Quantity Indexes 778 Summary 780 Contents xxi Glossary 780 Key Formulas 780 Supplementary Exercises 781 Chapter 18 Time Series Analysis and Forecasting 784 Statistics in Practice: Nevada Occupational Health Clinic 785 18. 1 Time Series Patterns 786 Horizontal Pattern 786 Trend Pattern 788 Seasonal Pattern 788 Trend and Seasonal Pattern 789 Cyclical Pattern 789 Selecting a Forecasting Method 791 18. 2 Forecast Accuracy 792 18. 3 Moving Averages and Exponential Smoothing 797 Moving Averages 797 Weighted Moving Average s 800 Exponential Smoothing 800 18. 4 Trend Projection 807 Linear Trend Regression 807 Holt’s Linear Exponential Smoothing 812 Nonlinear Trend Regression 814 18. Seasonality and Trend 820 Seasonality Without Trend 820 Seasonality and Trend 823 Models Based on Monthly Data 825 18. 6 Time Series Decomposition 829 Calculating the Seasonal Indexes 830 Deseasonalizing the Time Series 834 Using the Deseasonalized Time Series to Identify Trend 834 Seasonal Adjustments 836 Models Based on Monthly Data 837 Cyclical Component 837 Summary 839 Glossary 840 Key Formulas 841 Supplementary Exercises 842 Case Problem 1: Forecasting Food and Beverage Sales 846 Case Problem 2: Forecasting Lost Sales 847 Appendix 18. 1 Forecasting with Minitab 848 Appendix 18. 2 Forecasting with Excel 851 Appendix 18. 3 Forecasting with StatTools 852 xxii Contents Chapter 19 Nonparametric Methods 855 Statistics in Practice: West Shell Realtors 856 19. Sign Test 857 Hypothesis Test About a Population Median 857 Hypothesis Test with Matched Samples 862 19. 2 Wilcoxon Signed-Rank Test 865 19. 3 Mann-Whitney-Wilcoxon Test 871 19. 4 Kruskal-Wallis Test 882 19. 5 Rank Correlation 887 Summary 891 Glossary 892 Key Formulas 893 Supplementary Exercises 893 Appendix 19. 1 Nonparametric Methods with Minitab 896 Appendix 19. 2 Nonparametric Methods with Excel 899 Appendix 19. 3 Nonparametric Methods with StatTools 901 Chapter 20 Statistical Methods for Quality Control 903 Statistics in Practice: Dow Chemical Company 904 20. 1 Philosophies and Frameworks 905 Malcolm Baldrige National Quality Award 906 ISO 9000 906 Six Sigma 906 20. Statistical Process Control 908 Control Charts 909 _ x Chart: Process Mean and Standard Deviation Known 910 _ x Chart: Process Mean and Standard Deviation Unknown 912 R Chart 915 p Chart 917 np Chart 919 Interpretation of Control Charts 920 20. 3 Acceptance Sampling 922 KALI, Inc. : An Example of Acceptance Sampling 924 Computing the Probability of Accepting a Lot 924 Select ing an Acceptance Sampling Plan 928 Multiple Sampling Plans 930 Summary 931 Glossary 931 Key Formulas 932 Supplementary Exercises 933 Appendix 20. 1 Control Charts with Minitab 935 Appendix 20. 2 Control Charts with StatTools 935 Contents xxiii Chapter 21 Decision Analysis 937 Statistics in Practice: Ohio Edison Company 938 21. Problem Formulation 939 Payoff Tables 940 Decision Trees 940 21. 2 Decision Making with Probabilities 941 Expected Value Approach 941 Expected Value of Perfect Information 943 21. 3 Decision Analysis with Sample Information 949 Decision Tree 950 Decision Strategy 951 Expected Value of Sample Information 954 21. 4 Computing Branch Probabilities Using Bayes’ Theorem 960 Summary 964 Glossary 965 Key Formulas 966 Supplementary Exercises 966 Case Problem: Lawsuit Defense Strategy 969 Appendix: An Introduction to PrecisionTree 970 Chapter 22 Sample Survey On Website Statistics in Practice: Duke Energy 22-2 22. 1 Terminology Used in Sample Surveys 22-2 22. 2 Types of Surveys and Sampling Methods 22-3 22. Survey Errors 22-5 Nonsampling Error 22-5 Sampling Error 22-5 22. 4 Simple Random Sampling 22-6 Population Mean 22-6 Population Total 22-7 Population Proportion 22-8 Determining the Sample Size 22-9 22. 5 Stratified Simple Random Sampling 22-12 Population Mean 22-12 Population Total 22-14 Population Proportion 22-15 Determining the Sample Size 22-16 22. 6 Cluster Sampling 22-21 Population Mean 22-23 Population Total 22-24 Population Proportion 22-25 Determining the Sample Size 22-26 22. 7 Systematic Sampling 22-29 Summary 22-29 xxiv Contents Glossary 22-30 Key Formulas 22-30 Supplementary Exercises 22-34 Appendix: Self-Test Solutions and Answers to Even-Numbered Exercises 22-37Appendix A References and Bibliography 976 Appendix B Tables 978 Appendix C Summation Notation 1005 Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 1007 Appendix E Using Excel Functions 1062 Appendix F Computing p-Values Using Minitab and Exc el 1067 Index 1071 Preface The purpose of STATISTICS FOR BUSINESS AND ECONOMICS is to give students, primarily those in the fields of business administration and economics, a conceptual introduction to the field of statistics and its many applications. The text is applications oriented and written with the needs of the nonmathematician in mind; the mathematical prerequisite is knowledge of algebra.Applications of data analysis and statistical methodology are an integral part of the organization and presentation of the text material. The discussion and development of each technique is presented in an application setting, with the statistical results providing insights to decisions and solutions to problems. Although the book is applications oriented, we have taken care to provide sound methodological development and to use notation that is generally accepted for the topic being covered. Hence, students will find that this text provides good preparation for the study of more advanced statistical material. A bibliography to guide further study is included as an appendix.The text introduces the student to the software packages of Minitab 15 and Microsoft ® Office Excel 2007 and emphasizes the role of computer software in the application of statistical analysis. Minitab is illustrated as it is one of the leading statistical software packages for both education and statistical practice. Excel is not a statistical software package, but the wide availability and use of Excel make it important for students to understand the statistical capabilities of this package. Minitab and Excel procedures are provided in appendixes so that instructors have the flexibility of using as much computer emphasis as desired for the course.Changes in the Eleventh Edition We appreciate the acceptance and positive response to the previous editions of STATISTICS FOR BUSINESS AND ECONOMICS. Accordingly, in making modifications for this new edition, we have maintained the presentation style and readability of those editions. The significant changes in the new edition are summarized here. Content Revisions †¢ Revised Chapter 18 — â€Å"Time Series Analysis and Forecasting. † The chapter has been completely rewritten to focus more on using the pattern in a time series plot to select an appropriate forecasting method. We begin with a new Section 18. 1 on time series patterns, followed by a new Section 18. on methods for measuring forecast accuracy. Section 18. 3 discusses moving averages and exponential smoothing. Section 18. 4 introduces methods appropriate for a time series that exhibits a trend. Here we illustrate how regression analysis and Holt’s linear exponential smoothing can be used for linear trend projection, and then discuss how regression analysis can be used to model nonlinear relationships involving a quadratic trend and an exponential growth. Section 18. 5 then shows how dummy variables can be used to model seasonality in a foreca sting equation. Section 18. 6 discusses classical time series decomposition, including the concept of deseasonalizing a time series.There is a new appendix on forecasting using the Excel add-in StatTools and most exercises are new or updated. †¢ Revised Chapter 19 — â€Å"Nonparametric Methods. † The treatment of nonparametric methods has been revised and updated. We contrast each nonparametric method xxvi Preface †¢ †¢ †¢ †¢ †¢ †¢ †¢ †¢ with its parametric counterpart and describe how fewer assumptions are required for the nonparametric procedure. The sign test emphasizes the test for a population median, which is important in skewed populations where the median is often the preferred measure of central location. The Wilcoxon Rank-Sum test is used for both matched samples tests and tests about a median of a symmetric population.A new small-sample application of the Mann-Whitney-Wilcoxon test shows the exact sampling distrib ution of the test statistic and is used to explain why the sum of the signed ranks can be used to test the hypothesis that the two populations are identical. The chapter concludes with the Kruskal-Wallis test and rank correlation. New chapter ending appendixes describe how Minitab, Excel, and StatTools can be used to implement nonparametric methods. Twenty-seven data sets are now available to facilitate computer solution of the exercises. StatTools Add-In for Excel. Excel 2007 does not contain statistical functions or data analysis tools to perform all the statistical procedures discussed in the text.StatTools is a commercial Excel 2007 add-in, developed by Palisades Corporation, that extends the range of statistical options for Excel users. In an appendix to Chapter 1 we show how to download and install StatTools, and most chapters include a chapter appendix that shows the steps required to accomplish a statistical procedure using StatTools. We have been very careful to make the us e of StatTools completely optional so that instructors who want to teach using the standard tools available in Excel 2007 can continue to do so. But users who want additional statistical capabilities not available in standard Excel 2007 now have access to an industry standard statistics add-in that students will be able to continue to use in the workplace. Change in Terminology for Data.In the previous edition, nominal and ordinal data were classified as qualitative; interval and ratio data were classified as quantitative. In this edition, nominal and ordinal data are referred to as categorical data. Nominal and ordinal data use labels or names to identify categories of like items. Thus, we believe that the term categorical is more descriptive of this type of data. Introducing Data Mining. A new section in Chapter 1 introduces the relatively new field of data mining. We provide a brief overview of data mining and the concept of a data warehouse. We also describe how the fields of st atistics and computer science join to make data mining operational and valuable. Ethical Issues in Statistics.Another new section in Chapter 1 provides a discussion of ethical issues when presenting and interpreting statistical information. Updated Excel Appendix for Tabular and Graphical Descriptive Statistics. The chapter-ending Excel appendix for Chapter 2 shows how the Chart Tools, PivotTable Report, and PivotChart Report can be used to enhance the capabilities for displaying tabular and graphical descriptive statistics. Comparative Analysis with Box Plots. The treatment of box plots in Chapter 2 has been expanded to include relatively quick and easy comparisons of two or more data sets. Typical starting salary data for accounting, finance, management, and marketing majors are used to illustrate box plot multigroup comparisons. Revised Sampling Material.The introduction of Chapter 7 has been revised and now includes the concepts of a sampled population and a frame. The distincti on between sampling from a finite population and an infinite population has been clarified, with sampling from a process used to illustrate the selection of a random sample from an infinite population. A practical advice section stresses the importance of obtaining close correspondence between the sampled population and the target population. Revised Introduction to Hypothesis Testing. Section 9. 1, Developing Null and Alternative Hypotheses, has been revised. A better set of guidelines has been developed for identifying the null and alternative hypotheses.The context of the situation and the purpose for taking the sample are key. In situations in which the Preface xxvii †¢ †¢ †¢ †¢ focus is on finding evidence to support a research finding, the research hypothesis is the alternative hypothesis. In situations where the focus is on challenging an assumption, the assumption is the null hypothesis. New PrecisionTree Software for Decision Analysis. PrecisionTree is a nother Excel add-in developed by Palisades Corporation that is very helpful in decision analysis. Chapter 21 has a new appendix which shows how to use the PrecisionTree add-in. New Case Problems. We have added 5 new case problems to this edition, bringing the total number of case problems to 31.A new case problem on descriptive statistics appears in Chapter 3 and a new case problem on hypothesis testing appears in Chapter 9. Three new case problems have been added to regression in Chapters 14, 15, and 16. These case problems provide students with the opportunity to analyze larger data sets and prepare managerial reports based on the results of the analysis. New Statistics in Practice Applications. Each chapter begins with a Statistics in Practice vignette that describes an application of the statistical methodology to be covered in the chapter. New to this edition are Statistics in Practice articles for Oceanwide Seafood in Chapter 4 and the London-based marketing services company d unnhumby in Chapter 15. New Examples and Exercises Based on Real Data.We continue to make a significant effort to update our text examples and exercises with the most current real data and referenced sources of statistical information. In this edition, we have added approximately 150 new examples and exercises based on real data and referenced sources. Using data from sources also used by The Wall Street Journal, USA Today, Barron’s, and others, we have drawn from actual studies to develop explanations and to create exercises that demonstrate the many uses of statistics in business and economics. We believe that the use of real data helps generate more student interest in the material and enables the student to learn about both the statistical methodology and its application. The eleventh edition of the text contains over 350 examples and exercises based on real data.Features and Pedagogy Authors Anderson, Sweeney, and Williams have continued many of the features that appeare d in previous editions. Important ones for students are noted here. Methods Exercises and Applications Exercises The end-of-section exercises are split into two parts, Methods and Applications. The Methods exercises require students to use the formulas and make the necessary computations. The Applications exercises require students to use the chapter material in real-world situations. Thus, students first focus on the computational â€Å"nuts and bolts† and then move on to the subtleties of statistical application and interpretation. Self-Test ExercisesCertain exercises are identified as â€Å"Self-Test Exercises. † Completely worked-out solutions for these exercises are provided in Appendix D at the back of the book. Students can attempt the Self-Test Exercises and immediately check the solution to evaluate their understanding of the concepts presented in the chapter. Margin Annotations and Notes and Comments Margin annotations that highlight key points and provide ad ditional insights for the student are a key feature of this text. These annotations, which appear in the margins, are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text. xxviii PrefaceAt the end of many sections, we provide Notes and Comments designed to give the student additional insights about the statistical methodology and its application. Notes and Comments include warnings about or limitations of the methodology, recommendations for application, brief descriptions of additional technical considerations, and other matters. Data Files Accompany the Text Over 200 data files are available on the website that accompanies the text. The data sets are available in both Minitab and Excel formats. File logos are used in the text to identify the data sets that are available on the website. Data sets for all case problems as well as data sets for larger exercises are included. Acknowledgments A special thank you goes to Jeffrey D. Camm, University of Cincinnati, and James J.Cochran, Louisiana Tech University, for their contributions to this eleventh edition of Statistics for Business and Economics. Professors Camm and Cochran provided extensive input for the new chapters on forecasting and nonparametric methods. In addition, they provided helpful input and suggestions for new case problems, exercises, and Statistics in Practice articles. We would also like to thank our associates from business and industry who supplied the Statistics in Practice features. We recognize them individually by a credit line in each of the articles. Finally, we are also indebted to our senior acquisitions editor Charles McCormick, Jr. , our developmental editor Maggie Kubale, our content project manager, Jacquelyn K Featherly, our marketing manager Bryant T.Chrzan, and others at Cengage South-Western for their editorial counsel and support during the preparation of this text. David R. Anderson Dennis J. Sweeney Thomas A. Williams About the Authors David R. Anderson. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, he earned his B. S. , M. S. , and Ph. D. degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration at the University of Cincinnati. In addition, he was the coordinator of the College’s first Executive Program.At the University of Cincinnati, Professor Anderson has taught introductory statistics for business students as well as graduate-level courses in regression analysis, multivariate analysis, and management science. He has also taught statistical courses at the Department of Labor in Washington, D. C. He has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. Profe ssor Anderson has coauthored 10 textbooks in the areas of statistics, management science, linear programming, and production and operations management. He is an active consultant in the field of sampling and statistical methods. Dennis J.Sweeney. Dennis J. Sweeney is Professor of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B. S. B. A. degree from Drake University and his M. B. A. and D. B. A. degrees from Indiana University, where he was an NDEA Fellow. During 1978–79, Professor Sweeney worked in the management science group at Procter & Gamble; during 1981–82, he was a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and as Associate Dean of the College of Business Administration at the University of Cincinnati.Professor Sweeney has published more than 30 articles and monographs in the area of managem ent science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences, and other journals. Professor Sweeney has coauthored 10 textbooks in the areas of statistics, management science, linear programming, and production and operations management. Thomas A. Williams. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology.Born in Elmira, New York, he earned his B. S. degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his M. S. and Ph. D. degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed th e undergraduate program in Information Systems and then served as its coordinator. At RIT he was the first chairman of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis.Professor Williams is the coauthor of 11 textbooks in the areas of management science, statistics, production and operations management, and mathematics. He has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models. This page intentionally left blank STATISTICS FOR BUSINESS AND ECONOMICS 11e This page intentionally left blank CHAPTER Data and Statistics CONTENTS STATISTICS IN PRACTICE: BUSINESSWEEK 1. 1 APPLICATIONS IN BUSINESS AND ECONOMICS Accounting Finance Marketing Production Economics DATA Elements, Variables, and Observations Scales of Measurement Categorical and Quantitative Data Cross-Sectio nal and Time Series Data 1. DATA SOURCES Existing Sources Statistical Studies Data Acquisition Errors DESCRIPTIVE STATISTICS STATISTICAL INFERENCE COMPUTERS AND STATISTICAL ANALYSIS DATA MINING ETHICAL GUIDELINES FOR STATISTICAL PRACTICE 1 1. 4 1. 5 1. 6 1. 7 1. 8 1. 2 2 Chapter 1 Data and Statistics STATISTICS in PRACTICE NEW YORK, NEW YORK BUSINESSWEEK* With a global circulation of more than 1 million, BusinessWeek is the most widely read business magazine in the world. More than 200 dedicated reporters and editors in 26 bureaus worldwide deliver a variety of articles of interest to the business and economic community. Along with feature articles on current topics, the magazine contains regular sections on International Business, Economic Analysis, Information Processing, and Science & Technology.Information in the feature articles and the regular sections helps readers stay abreast of current developments and assess the impact of those developments on business and economic condit ions. Most issues of BusinessWeek provide an in-depth report on a topic of current interest. Often, the in-depth reports contain statistical facts and summaries that help the reader understand the business and economic information. For example, the February 23, 2009 issue contained a feature article about the home foreclosure crisis, the March 17, 2009 issue included a discussion of when the stock market would begin to recover, and the May 4, 2009 issue had a special report on how to make pay cuts less painful.In addition, the weekly BusinessWeek Investor provides statistics about the state of the economy, including production indexes, stock prices, mutual funds, and interest rates. BusinessWeek also uses statistics and statistical information in managing its own business. For example, an annual survey of subscribers helps the company learn about subscriber demographics, reading habits, likely purchases, lifestyles, and so on. BusinessWeek managers use statistical summaries from the survey to provide better services to subscribers and advertisers. One recent North *The authors are indebted to Charlene Trentham, Research Manager at BusinessWeek, for providing this Statistics in Practice. BusinessWeek uses statistical facts and summaries in many of its articles.  © Terri Miller/E-Visual Communications, Inc.American subscriber survey indicated that 90% of BusinessWeek subscribers use a personal computer at home and that 64% of BusinessWeek subscribers are involved with computer purchases at work. Such statistics alert BusinessWeek managers to subscriber interest in articles about new developments in computers. The results of the survey are also made available to potential advertisers. The high percentage of subscribers using personal computers at home and the high percentage of subscribers involved with computer purchases at work would be an incentive for a computer manufacturer to consider advertising in BusinessWeek. In this chapter, we discuss the types of d ata available for statistical analysis and describe how the data are obtained.We introduce descriptive statistics and statistical inference as ways of converting data into meaningful and easily interpreted statistical information. Frequently, we see the following types of statements in newspapers and magazines: †¢ The National Association of Realtors reported that the median price paid by firsttime home buyers is $165,000 (The Wall Street Journal, February 11, 2009). †¢ NCAA president Myles Brand reported that college athletes are earning degrees at record rates. Latest figures show that 79% of all men and women student-athletes graduate (Associated Press, October 15, 2008). †¢ The average one-way travel time to work is 25. 3 minutes (U. S. Census Bureau, March 2009). 1. 1 Applications in Business and Economics 3 †¢ A record high 11% of U. S. omes are vacant, a glut created by the housing boom and subsequent collapse (USA Today, February 13, 2009). †¢ The na tional average price for regular gasoline reached $4. 00 per gallon for the first time in history (Cable News Network website, June 8, 2008). †¢ The New York Yankees have the highest salaries in major league baseball. The total payroll is $201,449,289 with a median salary of $5,000,000 (USA Today Salary Data Base, April 2009). †¢ The Dow Jones Industrial Average closed at 8721 (The Wall Street Journal, June 2, 2009). The numerical facts in the preceding statements ($165,000, 79%, 25. 3, 11%, $4. 00, $201,449,289, $5,000,000 and 8721) are called statistics.In this usage, the term statistics refers to numerical facts such as averages, medians, percents, and index numbers that help us understand a variety of business and economic situations. However, as you will see, the field, or subject, of statistics involves much more than numerical facts. In a broader sense, statistics is defined as the art and science of collecting, analyzing, presenting, and interpreting data. Particul arly in business and economics, the information provided by collecting, analyzing, presenting, and interpreting data gives managers and decision makers a better understanding of the business and economic environment and thus enables them to make more informed and better decisions. In this text, we emphasize the use of statistics for business and economic decision making.Chapter 1 begins with some illustrations of the applications of statistics in business and economics. In Section 1. 2 we define the term data and introduce the concept of a data set. This section also introduces key terms such as variables and observations, discusses the difference between quantitative and categorical data, and illustrates the uses of cross-sectional and time series data. Section 1. 3 discusses how data can be obtained from existing sources or through survey and experimental studies designed to obtain new data. The important role that the Internet now plays in obtaining data is also highlighted. The uses of data in developing descriptive statistics and in making statistical inferences are described in Sections 1. 4 and 1. 5.The last three sections of Chapter 1 provide the role of the computer in statistical analysis, an introduction to the relative new field of data mining, and a discussion of ethical guidelines for statistical practice. A chapter-ending appendix includes an introduction to the add-in StatTools which can be used to extend the statistical options for users of Microsoft Excel. 1. 1 Applications in Business and Economics In today’s global business and economic environment, anyone can access vast amounts of statistical information. The most successful managers and decision makers understand the information and know how to use it effectively. In this section, we provide examples that illustrate some of the uses of statistics in business and economics. Accounting Public accounting firms use statistical sampling procedures when conducting audits for their clien ts.For instance, suppose an accounting firm wants to determine whether the amount of accounts receivable shown on a client’s balance sheet fairly represents the actual amount of accounts receivable. Usually the large number of individual accounts receivable makes reviewing and validating every account too time-consuming and expensive. As common practice in such situations, the audit staff selects a subset of the accounts called a sample. After reviewing the accuracy of the sampled accounts, the auditors draw a conclusion as to whether the accounts receivable amount shown on the client’s balance sheet is acceptable. 4 Chapter 1 Data and Statistics Finance Financial analysts use a variety of statistical information to guide their investment recommendations.In the case of stocks, the analysts review a variety of financial data including price/earnings ratios and dividend yields. By comparing the information for an individual stock with information about the stock market a verages, a financial analyst can begin to draw a conclusion as to whether an individual stock is over- or underpriced. For example, Barron’s (February 18, 2008) reported that the average dividend yield for the 30 stocks in the Dow Jones Industrial Average was 2. 45%. Altria Group showed a dividend yield of 3. 05%. In this case, the statistical information on dividend yield indicates a higher dividend yield for Altria Group than the average for the Dow Jones stocks. Therefore, a financial analyst might conclude that Altria Group was underpriced.This and other information about Altria Group would help the analyst make a buy, sell, or hold recommendation for the stock. Marketing Electronic scanners at retail checkout counters collect data for a variety of marketing research applications. For example, data suppliers such as ACNielsen and Information Resources, Inc. , purchase point-of-sale scanner data from grocery stores, process the data, and then sell statistical summaries of the data to manufacturers. Manufacturers spend hundreds of thousands of dollars per product category to obtain this type of scanner data. Manufacturers also purchase data and statistical summaries on promotional activities such as special pricing and the use of in-store displays.Brand managers can review the scanner statistics and the promotional activity statistics to gain a better understanding of the relationship between promotional activities and sales. Such analyses often prove helpful in establishing future marketing strategies for the various products. Production Today’s emphasis on quality makes quality control an important application of statistics in production. A variety of statistical quality control charts are used to monitor the output of a production process. In particular, an x-bar chart can be used to monitor the average output. Suppose, for example, that a machine fills containers with 12 ounces of a soft drink. Periodically, a production worker selects a sa mple of containers and computes the average number of ounces in the sample.This average, or x-bar value, is plotted on an x-bar chart. A plotted value above the chart’s upper control limit indicates overfilling, and a plotted value below the chart’s lower control limit indicates underfilling. The process is termed â€Å"in control† and allowed to continue as long as the plotted x-bar values fall between the chart’s upper and lower control limits. Properly interpreted, an x-bar chart can help determine when adjustments are necessary to correct a production process. Economics Economists frequently provide forecasts about the future of the economy or some aspect of it. They use a variety of statistical information in making such forecasts.For instance, in forecasting inflation rates, economists use statistical information on such indicators as the Producer Price Index, the unemployment rate, and manufacturing capacity utilization. Often these statistical ind icators are entered into computerized forecasting models that predict inflation rates. Applications of statistics such as those described in this section are an integral part of this text. Such examples provide an overview of the breadth of statistical applications. To supplement these examples, practitioners in the fields of business and economics provided chapter-opening Statistics in Practice articles that introduce the material covered in each chapter.The Statistics in Practice applications show the importance of statistics in a wide variety of business and economic situations. 1. 2 Data 5 1. 2 Data Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation. All the data collected in a particular study are referred to as the data set for the study. Table 1. 1 shows a data set containing information for 25 mutual funds that are part of the Morningstar Funds500 for 2008. Morningstar is a company that tracks over 7000 mutual funds and pre pares in-depth analyses of 2000 of these. Their recommendations are followed closely by financial analysts and individual investors. Elements, Variables, and Observations Elements are the entities on which data are collected.For the data set in Table 1. 1 each individual mutual fund is an element: the element names appear in the first column. With 25 mutual funds, the data set contains 25 elements. A variable is a characteristic of interest for the elements. The data set in Table 1. 1 includes the following five variables: †¢ Fund Type: The type of mutual fund, labeled DE (Domestic Equity), IE (International Equity), and FI (Fixed Income) †¢ Net Asset Value ($): The closing price per share on December 31, 2007 TABLE 1. 1 DATA SET FOR 25 MUTUAL FUNDS 5-Year Expense Net Asset Average Ratio Morningstar Value ($) Return (%) (%) Rank 14. 37 10. 73 24. 94 16. 92 35. 73 13. 47 73. 1 48. 39 45. 60 8. 60 49. 81 15. 30 17. 44 27. 86 40. 37 10. 68 26. 27 53. 89 22. 46 37. 53 12. 10 2 4. 42 15. 68 32. 58 35. 41 30. 53 3. 34 10. 88 15. 67 15. 85 17. 23 17. 99 23. 46 13. 50 2. 76 16. 70 15. 31 15. 16 32. 70 9. 51 13. 57 23. 68 51. 10 16. 91 15. 46 4. 31 13. 41 2. 37 17. 01 13. 98 1. 41 0. 49 0. 99 1. 18 1. 20 0. 53 0. 89 0. 90 0. 89 0. 45 1. 36 1. 32 1. 31 1. 16 1. 05 1. 25 1. 36 1. 24 0. 80 1. 27 0. 62 0. 29 0. 16 0. 23 1. 19 3-Star 4-Star 3-Star 3-Star 4-Star 3-Star 5-Star 4-Star 3-Star 3-Star 4-Star 3-Star 5-Star 3-Star 2-Star 3-Star 4-Star 4-Star 4-Star 4-Star 3-Star 4-Star 3-Star 3-Star 4-Star Fund Name American Century Intl.Disc American Century Tax-Free Bond American Century Ultra Artisan Small Cap Brown Cap Small DFA U. S. Micro Cap Fidelity Contrafund Fidelity Overseas Fidelity Sel Electronics Fidelity Sh-Term Bond Gabelli Asset AAA Kalmar Gr Val Sm Cp Marsico 21st Century Mathews Pacific Tiger Oakmark I PIMCO Emerg Mkts Bd D RS Value A T. Rowe Price Latin Am. T. Rowe Price Mid Val Thornburg Value A USAA Income Vanguard Equity-Inc Vanguard Sht-Tm TE Vangua rd Sm Cp Idx Wasatch Sm Cp Growth Fund Type IE FI DE DE DE DE DE IE DE FI DE DE DE IE DE FI DE IE DE DE FI DE FI DE DE WEB file Morningstar Data sets such as Morningstar are available on the website for this text. Source: Morningstar Funds500 (2008). 6 Chapter 1Data and Statistics †¢ 5-Year Average Return (%): The average annual return for the fund over the past 5 years †¢ Expense Ratio: The percentage of assets deducted each fiscal year for fund expenses †¢ Morningstar Rank: The overall risk-adjusted star rating for each fund; Morningstar ranks go from a low of 1-Star to a high of 5-Stars Measurements collected on each variable for every element in a study provide the data. The set of measurements obtained for a particular element is called an observation. Referring to Table 1. 1 we see that the set of measurements for the first observation (American Century Intl. Disc) is IE, 14. 37, 30. 53, 1. 41, and 3-Star.The set of measurements for the second observation (Ameri can Century Tax-Free Bond) is FI, 10. 73, 3. 34, 0. 49, and 4-Star, and so on. A data set with 25 elements contains 25 observations. Scales of Measurement Data collection requires one of the following scales of measurement: nominal, ordinal, interval, or ratio. The scale of measurement determines the amount of information contained in the data and indicates the most appropriate data summarization and statistical analyses. When the data for a variable consist of labels or names used to identify an attribute of the element, the scale of measurement is considered a nominal scale. For example, referring to the data in Table 1. , we see that the scale of measurement for the Fund Type variable is nominal because DE, IE, and FI are labels used to identify the category or type of fund. In cases where the scale of measurement is nominal, a numeric code as well as nonnumeric labels may be used. For example, to facilitate data collection and to prepare the data for entry into a computer databa se, we might use a numeric code by letting 1 denote Domestic Equity, 2 deno