Sylvia Plath s The Bell Jar - 1130 Words

Sylvia Plath is known as a profound writer, depicted by her lasting works of literature and her suicide which put her poems and novel of debilitating depression into a new perspective. In her poem â€Å"Lady Lazarus,† written in 1962, her mental illness is portrayed in a means to convey to her readers the everyday struggle of depression, and how it affects her view of her world, herself, and even those who attempt to tackle her battle with her. This poem, among other poetry pieces and her novel The Bell Jar, identify her multiple suicide attempts, and how the art of dying is something she has become a master of. Plath’s â€Å"Lady Lazarus,† about her trap of depression and suicide attempts, is effective and thought provoking because of her allusions to WWII Nazi Germany and the feelings of oppression and Nazism that the recurring images evoke. Beginning with the title, Plath takes a clear point of view as a Jewish person in the Holocaust. The Lazarus of Bethany, who was raised from the dead by Jesus, is the first allusion Plath incorporates. Lady Lazarus is a play on the Lazarus of Bethany, as Plath feels as if she has died several times from her failed suicide attempts, which she explains in the beginning of the poem. She believes she has tainted every decade of her life with an unsuccessful attempt, and can’t seem to go ten years without a new one. In the second stanza, she begins to introduce her allusions to the Holocaust and German Nazis, but not before she explains that she isShow MoreRelatedAnalysis Of Sylvia Plath s The Bell Jar 2248 Words   |  9 PagesTara Cameron Ms. Frega English 2.1 May 8, 2015 Sylvia Plath and Depression Sylvia Plath was a young and talented writer with the potential to exceed literary expectations. She was able to write a semi-autobiographical book about her struggle with depression and suicide, putting her personal story into the character of Esther Greenwood. The Bell Jar is the story of the hardships of a young woman named Esther who is clinically depressed and who struggles to keep up with the world around her. EstherRead MoreAnalysis Of Sylvia Plath s The Bell Jar 1573 Words   |  7 Pages How Sylvia Plath represent madness in the Bell Jar The book shows us a young girl who wants to be totally in charge of her own life where females were expected to be interesting and educated but only marry and be a good wife for ambitious men. She wants to enjoy life and experience every bit of it as she wants it to be. This would never work and in some ways she is born early. She would have been better in the ‘women s lib’ age ready for independence and happy of going places. Always able toRead MoreSylvia Plath s The Bell Jar1758 Words   |  8 PagesAmerica experienced similar oppressions, suppressions, questionings of worth and intelligence, and similar legal restrictions. American female authors such as Sylvia Plath and Adrienne Rich used their dexterous writing abilities to convey their feelings of displeasure in regards to women’s treatment in the 1950s and 1960s. In Sylvia Plath’s The Bell Jar, Esther Greenwood is a young woman of 1950s America, but she has thoughts, opinions, and feelings that do not align with those of her society. SimilarlyRead MoreSylvia Plath s The Bell Jar960 Words   |  4 PagesIn Sylvia Plath’s novel, The Bell Jar, Plath expresses her opposition to the idea of men having complete control over every aspect of women’s lives by utilizing the narrator Esther; a radical feminist, to speak out against conformity in a society run by men. Esther represents everything controversial about domesticity in the twentieth century. Throughout the novel she touches on taboo subjects such as radical feminism, rape, and resistance of patriarchal dictates, all of which were touchy topicsRead MorePersonal Growth Sy lvia Plath s The Bell Jar1177 Words   |  5 PagesPersonal Growth in Sylvia Plath’s The Bell Jar Sylvia Plath uses this quote in The Bell Jar to show the main character Esther Greenwood struggles. The quote states as followed,â€Å"There is something demoralizing about watching two people get more and more crazy about each other, especially when you are the only extra person in the room. It s like watching Paris from an express caboose heading in the opposite direction--every second the city gets smaller and smaller, only you feel it s really you gettingRead MoreSylvia Plath s The Bell Jar2369 Words   |  10 Pageshowever, Sylvia Plath may be one of the most iconic. Many believe living with debilitating mental illness can aid in creativity. Throughout Sylvia’s short life, she produced brilliant yet immensely troubled writing. Sylvia Plath’s struggle with both Bipolar Disorder and Depression is communicated within her writing through her use of creativity, visceral language, and emotional rawness. Her inner turmoil can be interpreted in her brilliant and vehemen ce evoking poetry as well as her novel, The Bell JarRead MoreSylvia Plath s The Bell Jar, And Her Other Works1413 Words   |  6 Pagesend† (Goodreads). In Sylvia Plath’s final days, the things she desired, did in fact annihilate her. Sylvia Plath desired perfectionism and the need to feel like she acquired a meaning. As interpreted in the novel, The Bell Jar, and her other works; Sylvia Plath parallels her own traumatic path throughout her life and her downward spiral during the 1950s, explaining her struggle with her mental suffocation and the inexorable depression that contaminated her mind. Sylvia Plath’s emotional turmoilRead MoreWomen s Sexual Experience By Sylvia Plath s The Bell Jar 918 Words   |  4 Pagesfaced in terms of their sexual experience. Through the eyes of the main character, Ester Greenwood, the novel focuses on the struggle between what women were beginning to gain and the antiquated notions of female purity and innocence. Ultimately, The Bell Jar critiques the gendered double standard women faced regarding sex in the mid-twenty-first century in its exploration of purity, equality, and freedom. The novel begins when Ester is nineteen and â€Å"pureness was the great issue† (82). She is encumberedRead MoreThe Cause Of Sylvia Plath s Depression1447 Words   |  6 PagesThe Causes of Sylvia Plath’s Depression When reading any works by Sylvia Plath, it is easy to focus on the depression of her writing. However, it is important to understand why she wrote most her works about depression. Plath based her works on her own life experiences. Sylvia Plath’s most commonly known book, The Bell Jar, is thought to be an autobiography. Aurelia Plath, Sylvia’s mother, published the book Letters Home, a collection of all the letters Sylvia wrote to her mother. The letters sheRead MoreThe Bell Jar by Sylvia Plath1211 Words   |  5 PagesSylvia Plath Research Paper Title The Bell Jar place[s] [the] turbulent months[of an adolescent’s life] in[to] mature perspective (Hall, 30). In The Bell Jar, Sylvia Plath uses parallelism, stream of consciousness, the motif of renewal and rebirth, symbolism of the boundary-driven entrapped mentally ill, and auto-biographical details to epitomize the mental downfall of protagonist, Esther Greenwood. Plath also explores the idea of how grave these timeless and poignant issues can affect a fragile

Mentorship Developing A Positive Learning Environment...

Mentorship can defined as a personal developmental relationship in which a more experienced or more knowledgeable person helps to guide a less experienced or less knowledgeable person. A mentor is a collaborative partner who is a role model and motivator providing support, help, enthusiasm, inspiration, and nurturing the clinical setting. â€Å"A mentor is also an active listener who will provide a safe, non-judgmental, friendly, and creative atmosphere for the new nurse† (Nursing Mentor, 2010, p. 1). The purpose of the Clinical Mentorship Committee is to develop a positive learning environment for nurses of all backgrounds and all different levels of experience. My thoughts are that it will not only be the mentors and their mentees who†¦show more content†¦It can also help decrease the amount of staff turnaround. Ever since Florence Nightingale established the nursing profession in the 1800’s, experienced nurses have been taking amateur nurses under their w ings and teaching them to become independent, caring and intelligent nurses. Body of the Paper Nursing is a practice discipline which requires ongoing development of knowledge and skills in order to provide quality care to patients. In order for this to occur, nurses need to develop certain skills to adapt to a continuously expanding knowledge-based practice. A large part of the learning process is done clinically, which for nurses right out of school requires the need for a supervised orientation with mentor who will provide sufficient and effective knowledge and skills these new professionals. At this time my facility has a three month orientation period where the new nurses are bounced around for lack of a better word. We have all been a part of this and are now in the place where we have a bigger voice and we are being heard that this is something that we can change to not only improve employee satisfaction but can also decrease the current turnaround rate that is increasing as the years go by. There have been a few of us that have tossed ideas around in the past ab out what we could do to implement this type of program. The hospital now recognizes that going from the classroom and a controlled clinical setting is completely

Davin Essay Example For Students

Davin Essay EVOLUTION. The theory of Evolution was thought up by Charles Darwin.He was born in Shrewsburg, England on February 12, 1809.He went to the University of Edinburgh for two years and to the University of Cambridge for the other two.He prepared to become a clergyman even though he was deeply interested in natural history.When he was twenty-three years old in the spring of 1831 Darwin was accepted to go on a ship that was captained by Robert Fritzroy.The ships name was The HMS Beagle.The purpose of the voyage was to survey the East and West coast of South America and the Pacific Islands but Darwins intention was to study different species of animals.During the voyage Darwin witnessed his first earthquake in Chili.He also figured out that some of the Islands he visited during the expedition were made from volcanic lava that took a very period of time to form.Darwin was so thoroughabout describing in his notes all his observations that he was able to write three books about South American Geology.Whe n Darwin reached the Galapagos Islands he made careful observations of the animal inhabiting the islands.In his time people thought that the world was only a few thousand years old.He proved them wrong when he noticed the different variations of fossils and animals.For example, he observed the difference in length of tortoises necks and different variations of the finches beaks.The change in both tortoises necks and finches beaks occurred because of the constant struggle for food.In the result of that struggle those species that survived and adapted to the environment were the fittest. Darwins voyage, which was supposed to last for two years, lasted for five.When he came home he continued his studies for years.Eventually his Theory of Evolution through natural selection was accepted all over the world.Charles Darwin died on April 19, 1882 but his work always remains central to the modern evolution theory.

Pride and Prejudice Movie Analysis free essay sample

But I will also be focusing on underlying traits that come around the surface when looking closely. pride [prahyd] Show IPA noun, verb, prid ·ed, prid ·ing. noun 1. a high or inordinate opinion of ones own dignity, importance, merit, or superiority, whether as cherished in the mind or as displayed in bearing, conduct, etc. [†¦] Now how is this character-trait projected throughout the language of film? Upon the first time we meet Darcy at the first ball, we are given a few visual clues in the first shot: Mr. Darcy’s back is the first thing we see of him. There is an extreme backlight that shows us only the silhouette of the man. This gave me the mysterious feeling of a character we did not know until now. It is a medium shot – slightly tilted from above. To represent pride, the angle needs to be condescending towards the ‘lower class’. We will write a custom essay sample on Pride and Prejudice Movie Analysis or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page This happens perfectly when Darcy stops walking and is ‘higher’ or ‘taller’ than everyone in the venue. We can feel him feeling raised above anyone else. Moving forward to the shot where we can see the front of Mr. Darcy. The medium shot stays and he is still taller than everyone else. The first close-up we get is after Mr. Darcy sees Elizabeth for the first time. The camera moves with him and the lighting is soft, representing that she could be his soft spot. As Mr. Darcy watches the dance commence, he stands in the shadows. Still tall, but slightly hidden away. This because he is a shy person and with his pride aside, he would like to disappear into the background. Also when Mr. Bingley talks to Elizabeth and Jane, Mr. Darcy is found in the background, darkened by light and blurred. This entire scene is shot inside, which reflects that Darcy is an inside person. He keeps in the dark, making him resentment towards attention. After the scene he could be seen as a vile, arrogant man. - Scene two The second scene I’ve used for this analysis the one of the last scenes with Mr. Darcy: the meadow scene. The first shot we see of him is a medium shot of him walking towards the camera. He is off-centered and there is a mystical, natural, light coming from the sunrise. The camera angle doesn’t make him look big anymore. It makes it look like he seems to have peace with the fact that he is just a man. The light gets brighter as the sun rises behind Mr. Darcy and could be seen how the revelation Elizabeth is having about him rises. Being outside instead of inside like he was the first time we met him, stands how he turned from the closed person he was to the open person he is now. At the end Elizabeth and Mr. Darcy stand together with the sun fully risen – indicating a new day and a new start for this new couple. Mr. Darcy is now everything but arrogant and is seen as the hero; a kind selfless man. - Comparison Binary Oppositions: Back – Front When meeting Mr. Darcy the first time, we are introduced to his back. This representing his closed personality. At the end however – we see him walking towards the camera. He’s no longer disappearing in closure, but open. Inside – Outside The decor is also important in watching Mr. Darcy evolve from what he is at the beginning of the movie and what he turns out to be in the end of the movie. In the beginning he is closed, locked up – and that is reflected in the closed ball-room. He is inside, so is his personality. At the end he has become more open: he is outside. He dares to share his feelings in the open sky (this reflecting to another scene where he declares his love to Elizabeth outside in the rain). This thought could represent Mr. Darcy’s fear of being locked up and he prefers to clear his mind out in the open. Artificial light – Natural light The ballroom scene is completely shot inside a house with artificial lights coming from chandeliers, giving it a bit of a gloomy feeling. The light is forced, and so is Mr. Darcy when he comes to the ball by his friends the Bingleys. The gloomy light also gives him opportunity to hide in the shades. When he is out in the open he comes at free will to Elizabeth. And the sun rising is a natural movement. The sun lightens the entire meadow – leaving no spot for Mr. Darcy to hide. - Conclusion In the beginning Mr. Darcy was a closed man – shown by keeping him inside and in the dark. Camera-angles portray him as a man who sees himself bigger than everyone. The setting is inside and the light is forced, creating dark spaces where he can hide. Mr.

Jazz and Drugs Over Time essays

Jazz and Drugs Over Time essays The purpose of this paper is to introduce, discuss, and analyze the topic of drug abuse and alcoholism during the jazz age. Specifically, it will discuss the history of abuse and its' effects on musicians and the music itself. Jazz music has a long and varied history in the United States, and unfortunately, it has a long history of drug and alcohol abuse among performers, too. This abuse helped give jazz an undeserved reputation among many listeners, but it also created a sub-culture among performers that has been difficult to overcome. Jazz is more than music and enticing rhythms, jazz is a state of mind for many, and that may be why so many jazz musicians and performers alter their states of mind with alcohol and drugs. Jazz music first came into being in the early 20th century, and the word was first noted around 1913 (Teachout 58). A jazz writer notes, "That word jazz is ambitious... The origin of the word is uncertain. The term has been applied also to noisy proceedings, to loud writing, to eccentric and discordant coloring'" (Osgood 10). Often performed by black musicians, jazz played a part in the Harlem Renaissance in New York, and remains one of the most popular forms of music today. However, jazz has always seemed to attract addictive personalities. The annals of jazz history are loaded with names synonymous with great music and addiction. Billie Holiday, Charlie Parker, Serge Chaloff, Chet Baker, Art Pepper, Miles Davis, John Coltrane, and Bill Evans are just a few of the jazz names associated with drug abuse, and many, many more musicians abused alcohol because it was so prevalent in the clubs they played. Alcoholism and drug addition bedeviled so many jazz musicians that the music came to be known for its grand beat and rhythm, but for its interest in addiction, too. One writer notes, "Historically, drug addiction seems to go hand-in-hand with jazz. Drug stories about Charlie Parker, Miles Davis, Chet B...

The Commonwealth of Nations - African History

The Commonwealth of Nations - African History What is the Commonwealth of Nations? The Commonwealth of Nations, or more commonly just the Commonwealth, is an association of sovereign states consisting of the United Kingdom, some of its former colonies, and a few special cases. The Commonwealth nations maintain close economic ties, sporting associations and complementary institutions. When was the Commonwealth of Nations Formed? In the early twentieth century, the government of Britain was taking a hard look at its relationship with the rest of the British Empire, and particularly with those colonies populated by Europeans – the dominions. The dominions had reached a high level of self-government, and the people there were calling for the creation of sovereign states. Even amongst the Crown Colonies, Protectorates, and Mandates, nationalism (and the call for independence) was on the rise. The British Commonwealth of Nations was first noted in the Statute of Westminster on 3 December 1931, which recognized that several of the United Kingdoms self-governing dominions (Canada, Australia, New Zealand, South Africa) were autonomous communities within the British Empire, equal in status, in no way subordinate one to another in any aspect of their domestic or external affairs, though united by a common allegiance to the Crown, and freely associated as members of the British Commonwealth of Nations. What was new under the 1931 Statute of Westminster was that these dominions would now be free to control their own foreign affairs – they were already in control of domestic affairs – and to have their own diplomatic identity. Which African Countries are Members of the Commonwealth of Nations? There are 19 African states who are currently members of the Commonwealth of Nations. See this Chronological List of African Members of the Commonwealth of Nations, or Alphabetical List of African Members of the Commonwealth of Nations for details. Is it Only ex-British Empire Countries in Africa Who Have Joined the Commonwealth of Nations? No, Cameroon (which had only partially been in the British Empire following World War I) and Mozambique joined in 1995. Mozambique was admitted as a special case (ie could not set a precedent) following democratic elections in the country in 1994. All its neighbors were members and it was felt that Mozambiques support against white-minority rule in South Africa and Rhodesia should be compensated. On the 28th November 2009 Rwanda also joined the Commonwealth, continuing the special case conditions under which Mozambique had joined. What Kind of Membership Exists in the Commonwealth of Nations? The majority of African countries who had been part of the British Empire gained independence within the Commonwealth as Commonwealth Realms. As such, Queen Elizabeth II was automatically the head of state, represented within the country by a Governor-General. Most converted to Commonwealth Republics within a couple of years. (Mauritius took the longest to convert – 24 years from 1968 to 1992). Lesotho and Swaziland gained independence as Commonwealth Kingdoms, with their own constitutional monarchy as head of state – Queen Elizabeth II was recognized only as the symbolic head of the Commonwealth. Zambia (1964), Botswana (1966), Seychelles (1976), Zimbabwe (1980), and Namibia (1990) became independent as Commonwealth Republics. Cameroon and Mozambique were already republics when they joined the Commonwealth in 1995. Did African Countries Always Join the Commonwealth of Nations? All those African countries still part of the British Empire when the Statute of Westminster was proclaimed in 1931 joined the Commonwealth except for British Somaliland (which joined with Italian Somaliland five days after gaining independence in 1960 to form Somalia), and Anglo-British Sudan (which became a republic in 1956). Egypt, which had been part of the Empire until 1922, has never shown an interest in becoming a member. Do Countries Maintain Membership of the Commonwealth of Nations? No. In 1961 South Africa left the Commonwealth when it declared itself a republic. South Africa rejoined in 1994. Zimbabwe was suspended on 19 March 2002 and decided to leave the Commonwealth on 8 December 2003. What Does the Commonwealth of Nations do for its Members? The Commonwealth is best known for the Commonwealth games which are held once every four years (two years after Olympic games). The Commonwealth also promotes human rights, expects members to meet a set of fundamental democratic principles (curiously enough spelt out in the Harare Commonwealth declaration of 1991, given Zimbabwes subsequent departure form the association), to provide education opportunities, and maintain trade links. Despite its age, the Commonwealth of Nations has survived without needing a written constitution. It depends upon a series of declarations, made at Commonwealth Heads of Government Meetings.

Career Goals and Future Aspirations Essay Example | Topics and Well Written Essays - 250 words

Career Goals and Future Aspirations - Essay Example Econometrics and time series modules, Advanced Financial Models and Financial Reporting, Derivatives, Economic Foundations and Management Practice, among others. These course modules would assist in achieving one’s aims through the honing of skills in financial concepts, cognitive and analytical skills in evaluation of financial statements, developing in-depth understanding of trading, specifically buying and selling shares, bonds and assets for different investors, and in harnessing management skills including applications of planning, organizing, directing and controlling functions. Finally, I have plans to take examinations of the Charted Financial Analyst where Judge Business School’s curriculum has been acknowledged to incorporate a large part of CFA Level 1 curriculum that would be instrumental in progressing in my career path. Besides the expertise in the finance area, Judge Business School would appropriately provide an unparalleled platform to network with the right connections in the financial field, which would widen my professional experience and eventually accord a competitive advantage to practice my craft as an excellent financial trader on a global sphere.

IKEA Stores Layout and Sizes Assignment Example | Topics and Well Written Essays - 2500 words

IKEA Stores Layout and Sizes - Assignment Example It was impressed upon his young mind that the most should be made out of the limited resources and that the essentials of modern living may be acquired at reasonable cost. From the time he set up his first business in the 1930s and registered as IKEA in 1943, Kamprad’s overriding strtegy was to adopt every cost-cutting solution that did not compromise quality and innovative ideas (Ikea.com, 2012). The diagram that follows shows IKEA’s key strategic thrusts by which it seeks to flesh out Kamprad’s vision. Central to the strategy is the simple and creative design which is well-received by the market; it is distributed through large stores with a wide range of products, all priced inexpensively, designed in flat packs, and requiring customer assembly. IKEA’s Strategic Direction (http://sites.google.com/site/faizahmadali/IKEA.JPG) The manner by which the stores provide customer accessibility to a wide selection of useful products, and the manner by which the p roducts are inexpensively priced, easily stored and transported through flat packed boxes, and engage end-user participation in their assembly all contribute to customer engagement through low cost, durable quality, and aesthetic appeal. 2. Three organizational tensions, and how the strategic direction addresses them. The diagram on the next page shows a strategic map of the company, identifying in blue the central goal of providing furniture and accessories for the home, the four principal strategic objectives arranged in a square around the goal, and the elements that support the goal and objectives. http://www.monografias.com/trabajos89/strategy-michael-porter/image011.jpg The elements that contribute to the realization of the objectives or goals provide clues to various organizational tensions between the firm and its stakeholders, due to factors both internal and external to the organisation. By tension is meant the existence of clashing interests between stakeholders and the c ompany. For IKEA, some customers have taken issue with (and even ridiculed) the manner by which IKEA products presume the customers’ adequate capability in assembling the product. This creates tension in the need to design easy-to-assemble units vis-a-vis the need to engage customer participation in the assembly process. Internal IKEA store layout featuring products’ flatpack design (Facenda, 1999) A second source of tension is the need to create a variety of designs, which clashes with the need to reduce manufacturing costs. Ordinarily, cost reduction is best achieved through product standardization, rather than product diversification needed to produce a variety of products . By seeking to diversify but at the same time mass-produce, tensions are created between the production unit of the firm and the marketing unit which identifies the variety of product lines offered in IKEA stores. Finally, a third source of tension is in the size of IKEA stores and its repercussi ons upon the community. The size of IKEA stores are as a rule large enough to enable customers to access all possible

The Castle Essay Example for Free

The Castle Essay â€Å"Texts convey certain attitudes and beliefs that help define who we are and how we relate to the world around us† Discuss the attitudes and beliefs that are highlighted in you prescribed text and two related texts of your own choosing. The term global village refers to the idea that individual countries and communities are affected by the media, electronic communications and cheap air travel that their traditions and beliefs are challenged. These challenges may be positive or negative as it makes people to reassess their attitudes and beliefs. There have been numbers of areas of challenges and two of them are food and multi-culturalism. These challenges are explicitly presented in the film directed by Rob Stitch, The Castle, and a number of related materials. One aspect of the global village which is effectively represented by Sitch in The Castle is the attitude towards the food from diverse cultures which exists in Australia. Kerrigan family is very contented with their rather bland and preservative diet which they share in their family home. Sitch represents this situation by repeatedly filming the dining of Kerrigan family. Sitch focuses on the food menus and they are just ordinary Australian food. This shows that Kerrigan family has yet not encountered many diverse food cultures such as Vietnamese and Thai which exists in Australia. Dale, the narrator, comments in an enthusiastic tone that, â€Å"Dad thinks mum is the greatest cook on earth† so when she serves them a rather ordinary looking cake Daryl asks, â€Å"what do you call that, darl? ’† Sal’s simple answer of â€Å"Sponge cake† sums up that this family have yet to really come to grips with the astronomical influence of the global village on Australian cuisine. He states glowingly just how he feels about Sal’s cooking when he says, â€Å"Why go out to a restaurant when this keeps coming up night after night†. Eating meals together is represented as an important family tradition. Daryl’s attitude strongly contrasts with the words of the song from Scene Four of Noelle Janaczewska’s play Blood Orange. In this short play, it explores aspects of global village in Australia. Repetition of ‘Coles is selling tabouli, lemon grass and parmesan cheese! ’ accentuates that a whole range of food from different ethnic backgrounds exists in Australia and even one of Australian food market ‘Coles’ is selling them. Cultural challenges to taste buds are certainly another strong influence in increasing tolerance and acceptance of Australia’s different ethics groups and cultures which are consistently reaffirming that Australia is indeed an excellent example of the concept of the global village. Another aspect of the global village which emerges from the experiences of the Kerrigan family in the film The Castle is that Australia is a multi-cultural country with immigrants from a range of countries. Sitch reveals multi-culturalism in Australia through various characters in the film. The Kerrigans are a very self sustaining family but even they have to open their door and minds to people of different ethnic backgrounds. Sitch is able to represent this in the way Daryl interacts easily with ‘new Australians’. Daryl is portrayed making a real effort at Tracey and Con’s wedding to show his acceptance of a different culture by commenting jokingly on the Greek tradition of breaking plates. He also learned ‘Good Evening’ in Greek which pleased Con’s family. Daryl welcomes Con to their family saying that while Con might be different â€Å"anyone who loves our Trace as much as us deserves our love. So we love you Con. We love you†. Sitch celebrates in a positive way this genuine acceptance of others in the way Con is presented as one of the family up at Bonny Doon and in the family home on returning from Thailand; he is obviously one of the family. Farouk is a neighbour of the Kerrigans and he too is a part of Daryl’s circle of neighbours. Farouk comes to Daryl for leadership and help with English when the letters of compulsory acquisition arrives. Daryl does the same assisting Jack and Yvonne who also are his neighbours. Therefore Daryl’s leadership and kindness to others in his neighbourhood, whether they are Australians or immigrants shows the vibrant part of the global village. An article â€Å"I now call Australia home†, written by Nick Gianopoulos is a relevant piece of related material on the global village which supports the idea of Australia as a multi-cultural country. Gianopoulos talks of the difficulty growing up as a son of Greek immigrant during the 60’s from racism. Similarly with Daryl Kerrigan, Gianopoulos says that Australia has changed to accept people from around the world as part of the Australian mainstream. â€Å"Our cultures are better understood. We’ve even become trendy’. He also believes that we now need to continue to extend that understanding and acceptance to our newest Australians from Asia. Daryl’s and Giannopoulos’ experiences strongly reflect upon the aspect of global village that Australia is a multi-cultural country. As Australia rapidly grows into a multi-cultural country, people are challenged to their beliefs and attitudes. In conclusion Sitch’s film The Castle and related materials â€Å"Blood Orange† and â€Å"I now call Australia home† has effectively revealed and represented how the two aspects of global village could challenge people’s attitudes and beliefs. Through food, it showed the difficulty that Kerrigan’s family encountered. However Kerrigans adapted well to accept multi-culturalism into their neighbourhood and family.

Globalization and Islamic Fundamentalism Essay -- Muslim Culture Islam

The Al-Qaeda offshoot ISIS, has made its way through Iraq and Syria. This new terror campaign appears to have been rolled out with a decades old objective, which is wrought with violence, propaganda and destabilization. But what are the reasons behind these acts of terror and violence? How is it possible to stop terrorism? What is the future of the endless conflict between Islamic extremism and modernity? The last one is particularly burning, since it touches an issue, entwined in ever-lasting controversy, aggression and needless carnage - the issue of Islamic fundamentalism and its extreme manifestation - terrorism. In my paper I argue that in its essence Islamic fundamentalism is a negation of the values, upheld by globalization, democracy, true Islam and modernity. There are several interconnected focal factors that render Islamic extremism incompatible with modern trends of development. The first and basic factor, always in the context of the history of Islam, is the theoretical foundation of fundamentalism. Also, another important issue is the rise of Islamic nationalism, which helps fundamentalism transform religious and cultural differences into an overt and brutal struggle against non-Muslim countries and their globalizing world. Of particular significance, however, is the poor economic development of Muslim states. Moreover, I believe it is the actual reason behind the violent outbreak of extremism. All these issues combine to form the idea of Islamic ideal versus reality, or the ever-increasing gap between modernity and the nature of fundamentalism. In addition, I argue that poor economic and social conditions in Muslim countries are caused by improper government policies and deep social c... ...://www.worldbank.org/wbi/mdf/mdf1/edecmen.htm> Global Poverty Monitoring. The Middle East and North Africa: An Overview. United Nations Development Programme. Human Development Report 2001 United States Senate. Extremist Movements and Their Threat to the United States. Washington: U.S. Government printing Office, 2000 The Islamic World to 1600: The Rise of the Great Islamic Empires The New York Times; October 31, 2001

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