interstellar pig :: essays research papers

Interstellar Pig Funky StuffbyOutrageously the most entertainingly fiction that soon enough real life book that I pay ever read. This book was outstandingly filled with mysteries and packed with entertainment for the reader.Barney, a teenage boy, and his parents rent a summer cabin on cardinal of Californias beaches. They realize that their neighbors wanted this cabin very(prenominal) much but Barney and his parents ended up acquire it. When Barney meets his neighbors he thinks that they are a little bit weird but his parents dont think that they are too bad of people in fact they sort of like them. His neighbors also have a very strange game they play thats called Interstellar Pig it is a very strange game. Their neighbors are about in their mid-twenties there is one girl, Zena, and two boys, Joe and Manny. Barney finds out that these neighbors wanted his house because there is some hidden secret in it ,which Barney doesnt have a clue what it is but one day finds out about a little, well big, drawing that points to an island off of one of the coasts by his house about one or two miles away. When his neighbors find out about this they want to go there right away but without Barney, but does Barney go, what do they find at this island, what happens after they find out whats there? And what happens with this weird game they play?The characters picked for this book couldnt have been better. Their descriptions and everything else add together so perfectly. I dont think anything could have fit better. William Sleator did a wonderful job of writing this book. He just fit everything including the characters in very well.When I first got this book from the library I did it because I had about ten seconds left in my study hall and call for a book for English class so I just grabbed it off of the wall. My first intentions where what kind of stupid book is this by just face at the cover but about two chapters into it I really got into it and liked it.

The Haitian Relationship With the Dominican Republic Essays -- Politic

The Haitian Relationship With the friar preacher RepublicThe Haitian revolution had tremendous repercussions in the social, political and economic arenas of the world, but especially for the birth with the neighboring nation of the Dominican Republic. In order to understand the development of the Dominican-Haitian relationship after the Haitian revolution one must examine how the devil colonies of Hispanola dealt with each other before it. Throughout history there has been constant stress between the interactions of these nations, yet there is no easy explanation for what has caused it. In effect, it has been an accumulation of events which has allowed for the present relationship to evolve.By the 1780s Saint Domingues had the largest amount of slaves in the Caribbean. This large amount of slaves can be extensively attributed to the tight 30,000 Africans imported to the colony between 1785-1790 (Beckles 403) . This extraordinary amount of slaves allowed Saint Domingue emerge as o ne of the wealthiest colonies of its time, but it also made the island susceptible to a roaring upheaval for the transplanted African communities. In 1789 Saint Domingue had approximately 8,000 plantations which produced crops for export which generated two fifths of Frances foreign trade, a proportion rarely equalled in any colonial imperium (Beckles 403). The majority of crops being exported were coffee, and sugar although cotton, indigo were also part of this colonies economic prosperity. The majority of the nearly 500,000 slaves on the island, at the end of the eighteenth century endured almost of the worst slave conditions in the Caribbean. These people were seen as disposable economic inputs in a colony driven by greed. Thus, they receive... ...nue to occur which has developed great tension between these neighboring nations.Works CitedBeckles, Dr. Hillary, Verene Shepherd. Caribbean Slave Society and Economy. The New Press, New York. New York, N.Y. 1991. Bethell, Leslie. T he Cambridge History of Latin America Vol. III. Cambridge University Press, London, England. 1985. Logan, Rayford. Haiti And The Dominican Republic. Oxford University Press, New York, NY. 1968. http//www.uhhp.com/haitrev1.html http//caribbeansupersite.com/domrep/history.htm - Dominican History http//www.uhhp.com/haitrev1.html - Haitian Revolution http//www.lib.utexas.edu/Libs/PCL/Map_collection/americas/Haiti.GIF Haitian Map (Large) http//caribbeansupersite.com/haiti/history.htm -Haitian History http//www.eurohost.com/imagesof/flags/anthems.html -National anthems of Haiti and Dominican Republic

One Flew Over the Cuckoo’s Nest Essay -- Essays Papers

One Flew everywhere the repeats nearWritten by Ken Kesey, One Flew Over the Cuckoos draw near was published in 1967 by Penguin Books. This story was written based on the authors experience while working in a noetic institution. He held long conversations with the inmates in order to gain a better understanding of them. It was during this period that he wrote the first draft of One Flew Over the Cuckoos Nest. Most of the characters in the novel are based upon actual patients he met while working at the hospital. One Flew Over The Cuckoos Nest is delimit in a mental hospital in Oregon. The novel is divided into four parts. Parts One, Two and Four are set in the hospital itself. In Part Three, the patients from the hospital go on a deep-sea fishing trip, and the setting is the boat. Except for a few outsiders, the characters are every patients or employees of the hospital. Kesey has drawn from his own experience to give the reader an insiders view of the hospital. The novel starts with the admission of Randle P. McMurphy to the Hospital. As he introduces himself to the other patients, the head nurse, lactate Ratched right away decided he is a troublemaker. Even though everyone else is afraid of the nurse, everyone that is except for McMurphy. He tires to make as much commotion as he can. He sings when hes not supposed to, asks for things when its not time to, and appears half-naked, which really flusters the nurse. When a staff meeting comes up, the do... One Flew Over the Cuckoos Nest Essay -- Essays PapersOne Flew Over the Cuckoos NestWritten by Ken Kesey, One Flew Over the Cuckoos Nest was published in 1967 by Penguin Books. This story was written based on the authors experience while working in a mental institution. He held long conversations with the inmates in order to gain a better understanding of them. It was during this period that he wrote the first draft of One Flew Over the Cuckoos Nest. Most of the charac ters in the novel are based upon actual patients he met while working at the hospital. One Flew Over The Cuckoos Nest is set in a mental hospital in Oregon. The novel is divided into four parts. Parts One, Two and Four are set in the hospital itself. In Part Three, the patients from the hospital go on a deep-sea fishing trip, and the setting is the boat. Except for a few outsiders, the characters are either patients or employees of the hospital. Kesey has drawn from his own experience to give the reader an insiders view of the hospital. The novel starts with the admission of Randle P. McMurphy to the Hospital. As he introduces himself to the other patients, the head nurse, Nurse Ratched immediately decided he is a troublemaker. Even though everyone else is afraid of the nurse, everyone that is except for McMurphy. He tires to make as much commotion as he can. He sings when hes not supposed to, asks for things when its not time to, and appears half-naked, which reall y flusters the nurse. When a staff meeting comes up, the do...

Is Development Methodologies In Financial :: essays research papers

Introduction & Overview of the CompanyMarks and Spencers is a large UK based retailer with 683 branches in 2 continents. Following the deregulation of the UK financial sector in the mid eighties the company decided to use its experience, capital, and brand power to branch into the lucrative financial services industry offering personal loans, disembodied spirit insurance & pensions, and savings & investments services such as Unit Trusts and ISAs through the company Marks and Spencers financial Services. Financial Services is now one of the fast expanding areas of Marks & Spencers, MSFS employs more than 1,400 staff at its purpose built headquarters in Chester, and has dedicated financial services areas in 70 M&S stores across the country. The Information Systems department has 50 employees who come from analysis, design, & programming backgrounds, much of which has been gained with Marks & Spencers plc. The majority of these employees are based in the Chester head office, and sys tems are true &8216in-house&8217. IS projects under development include the mental hospital of Individual Savings Accounts, with other likely future projects including telephone banking, credit cards, and auto/property insurance.The fact that MSFS has entered the Financial Services Sector comparatively recently and with a established IS knowledge base from the parent company has meant that subsisting information systems have been well certain in terms of technology and are compliant with the latest industry regulations. Therefore there is little or no need for redevelopment of existing systems in the short to medium term, and the ISD focus is almost exclusively on new market areas requiring Information Systems that can be started from scratch. The main exceptions to this are the chance of introducing data warehousing to tap the potential of both MSFS&8217 and the parent company&8217s client database to better target MSFS customers, and the need to adapt existing systems for the introduction of the Euro, projects which will involve redesign of existing data stores and software.The company has grown very quickly, and IS projects have consequently grown considerably as can be seen by the size of the IT department. While there are still small to medium sized projects, some that are currently being considered will be on quite a large scale with several senior analysts working on each project. These projects are anticipated to require an IT department amplification of 50% over the next two years.Although MSFS&8217 IS projects share certain common characteristics in terms of their requirements, such as the need for a common ISD social system across projects, they vary considerably in size and strategic importance, with future developments looking likely expand those differences.

Free Candide Essays: Impossibility Of The Happy Life :: Candide essays

Candide The Impossibility Of The Happy Life    This papers focus is Voltaires view of humanity gratification.  Specific exclusivelyy, it will argue that Voltaire, in Candide, says that human blessedness is impossible. Voltaire believes this for three reasons. First, Voltaire presents mankind in the story spending all its life worried about personal problems of the moment. When people in Candide have no problems, Voltaire indicates, they do not feel happy but become bored instead. Their emotional lasts swing between worries and boredom with almost no periods of prolonged happiness. Secondly, Voltaire believes human happiness is impossible because the world as he presents it in Candide is full of selfish people whose actions spoil the well being of all their fellow human beings. Thirdly, Voltaire believes human happiness is impossible because governments are so violent and organized religion is so corrupt that they ruin the lives of millions through war and exploitat ion.               These points may be luxuriously demonstrated through an analysis of Candide itself and also through the views of important critics. To best appreciate this novel, however, some background concerning its origins and its relationship to the authors preoccupations should be mentioned.               Francois Marie Arouet de Voltaire lived from 1694-1778. He was an author and a philosopher whose philosophy stressed rationality, democracy and scientific inquiry. These interests can all be seen in Candide, for example, which has a philosopher for a main character and which satirizes the philosophy of Leibnitz throughout the text. The novel Candide was written in response to the earthquake of 1759 which hit Lisbon and resulted in the instantaneous and indiscriminate deaths of thousands. Appalled by the horrible deaths of so many transparent people, Voltaire was at this time also incensed by Leibnitz who wrote that given the worlds God might have created, by choosing to endow mankind with free will, the world we live in is the best of all possible worlds. To Voltaire, this response to the earthquake amounted to an abominable moral complacency and indifference by philosophers such as Leibnitz, who Voltaire felt seemed to take away all the other normal suffering and injustice in the world. Hence in Candide, Voltaire relentlessly satirizes Leibnitzs formulation by shifting the stress to this is the best of all possible worlds and bringing up the line every time a character encounters a horrible calamity or atrocity. However, it should be added that Voltaires hatred of injustices perpetrated by the aristocracy, the church and the state--all of which he satirizes in Candide--also grew out of his personal experiences.

Free Candide Essays: Impossibility Of The Happy Life :: Candide essays

Candide The Impossibility Of The Happy Life    This papers focus is Voltaires view of human happiness.  Specifically, it will argue that Voltaire, in Candide, says that human happiness is impractical. Voltaire believes this for three reasons. First, Voltaire presents existence in the novel spending all its life worried about personal problems of the moment. When people in Candide have no problems, Voltaire indicates, they do not feel happy but become bored instead. Their emotional lives swing between worries and boredom with almost no periods of prolonged happiness. Secondly, Voltaire believes human happiness is impossible because the solid ground as he presents it in Candide is full of selfish people whose actions spoil the well being of all their fellow human beings. Thirdly, Voltaire believes human happiness is impossible because governments are so violent and organized religion is so corrupt that they ruin the lives of millions through and through war and exploi tation.               These points may be amply demonstrated through an analysis of Candide itself and also through the views of important critics. To outperform appreciate this novel, however, some background concerning its origins and its relationship to the authors preoccupations should be mentioned.               Francois Marie Arouet de Voltaire lived from 1694-1778. He was an author and a philosopher whose philosophy accentuate rationality, democracy and scientific inquiry. These interests can all be seen in Candide, for example, which has a philosopher for a main character and which satirizes the philosophy of Leibnitz throughout the text. The novel Candide was written in response to the earthquake of 1759 which hit Lisbon and resulted in the instantaneous and indiscriminate deaths of thousands. Appalled by the horrible deaths of so many innocent people, Voltaire was at this c artridge clip also incensed by Leibnitz who wrote that given the worlds God might have created, by choosing to endow mankind with free will, the world we live in is the best of all possible worlds. To Voltaire, this response to the earthquake amounted to an abominable moral complacency and indifference by philosophers such as Leibnitz, who Voltaire felt seemed to accept all the new(prenominal) normal suffering and injustice in the world. Hence in Candide, Voltaire relentlessly satirizes Leibnitzs formulation by shifting the stress to this is the best of all possible worlds and legal transfer up the line every time a character encounters a horrible calamity or atrocity. However, it should be added that Voltaires hatred of injustices perpetrated by the aristocracy, the church and the state--all of which he satirizes in Candide--also grew out of his personal experiences.

Life in the 1920s in Melbourne was much different than the years before

There were drastic changes in Melbourne and also the world. Several changes made were that there is more ways for people to entertain themselves, women started to rupture differently, also the form of conveyance was different.People in the 1920s had many ways to entertain themselves. Several ways they entertained themselves was by watched the football (which was called the VFL because it was only straight-laced teams), going to the movies and watching the Melbourne Cup. The VFL is like AFL today but in the 1920s it wasnt Australia wide. Some of the teams that played were Collingwood, Carlton, Geelong, Essendon, South Melbourne, Richmond, St Kilda, Fitzroy and Melbourne. In the 1920s Richmond won the Grand Final that year, Collingwood were the runners up that year. Collingwood lost by 17 points to a crowd of 53,908. The best player during this time was Roy Cazaly George Bayliss was the leading goal scorer in 1920.Cinemas in Melbourne during the 1920s were in black and white. There was also no sound that was made by the actors. The only sound that they heard was from a piano player that on the stance of the screen and played music when it was the right time. Many of the movies seen in the cinemas were from America but there were a couple that were made in Australia. One of the movies shown in cinemas during the 1920s was Soldiers of the Cross the main characters in this movie were Beatrice Day, Harold Graham. Also in the 1920s the Melbourne Cup was won by a horse named Poitrel, the jokey that was riding him was K.Bracken and the trainer was H.J.Robinson. Erasmus came second and top executive comedy came third.Most of the transport in the 1920s was by trains and cars. The trains they had been stream trains which were loud and let out a lot of smoke from their chimneys. Trains only travelled at a few kilometres an hour but were gradually changed to 30km/h during the 1920s. Flinders Station existed during that time and is still used today. Many Australians had cars, it was said that about 500 000 cars were own in 1929 by Australians. Australia was ranked in the top five nations that owned cars. Most of the cars in Australia were imported from Europe and America but also any(prenominal) were made here in Australia. Most of the cars were run on steam but they began to move towards the petrol cars. Plans were used as a source of transport to go to other countries. Throughout the late 1920s electric trams started to appear in MelbourneFashion for men and women changed enormously clean-shaven chins became more fashionable than beards and knee-length skirts were heights fashion for women. Coats and stoles became fashionable in Melbourne. The ideas of these types of clothing came from the Chinese, Egyptian and the Japanese. Coats were transformed into a more casual which were made from lightweight silks and local fur. They also had coats that had a sensual combination of Chinese, Egyptian and Australian influences.

Statistics for Business and Economics

Openmirrors. com ac cumulative PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION Cumulative probability Entries in this table give the argona 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 . 1 788 . 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 argona 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 . 9382 . 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 . 535 9 . 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 intention in exclusivelyy left blank STATISTICS FOR BUSINESS AND ECONOMICS 11e David R. Anderson University of Cincinnati Dennis J. Sweeney University of Cincinnati Thomas A. Williams Rochester give of Technology Statistics for care and Economics, Eleventh Edition David R. Anderson, Dennis J. Sweeney, Thomas A.Williams VP/ chromatography column 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 action House/Compositor MPS Limited, A Macmillan phoner Senior Art Director Stacy Jenkins Shirley Internal Designer Michael Stratton/cmiller design C everywhere Designer Craig Ramsdell Cover Images Getty Images/GlowImages Photography Manager John Hill 2011, 2008 South-Western, Cengage encyclopaedism ALL RIGHTS RESERVED. No part of this work cover by the copyright herein may be reproduced, transmitted, stored or used in any wee or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, s back toothning, digitizing, taping, Web distribution, information networks, or information storage and retrieval systems, pull up 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 & gross sales Support, 1-800-354-9706 For permission to use material from this text variant or product, submit all requests online at cengage. com/permiss ions Further permissions questions stub be emailed to emailprotected 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 argon registered trademarks of Apple Computer, Inc. used herein under license. program library of Congress Control Number 2009932190 Student Edition ISBN 13 978-0-324-78325-4 Student Edition ISBN 10 0-324-78325-6 Instructors Edition ISBN 13 978-0-538-45149-9 Instructors Edition ISBN 10 0-538-45149-1 South-Western Cengage Learning 5191 Natorp avenue 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 int entionally left blank Brief limitPreface xxv some the Authors x 19 Chapter 1 selective information and Statistics 1 Chapter 2 Descriptive Statistics Tabular and lifelike Presentations 31 Chapter 3 Descriptive Statistics Numerical Measures 85 Chapter 4 Introduction to luck 148 Chapter 5 Discrete chance Distributions 193 Chapter 6 Continuous Probability Distributions 232 Chapter 7 ingest and taste Distributions 265 Chapter 8 Interval Estimation 308 Chapter 9 surmise screen outs 348 Chapter 10 Inference About Means and Proportions with twain macrocosms 406 Chapter 11 Inferences About macrocosm Variances 448 Chapter 12 Tests of truth of Fit and independence 472 Chapter 13 Experimental Design and summary of Variance 506 Chapter 14 Simple bi additive reversion 560 Chapter 15 ninefold Regression 642 Chapter 16 Regression summary ModelBuilding 712 Chapter 17 Index Numbers 763 Chapter 18 succession serial publication Analysis and predict 784 Chapter 19 Nonparametric syst ems 855 Chapter 20 statistical Methods for woodland Control 903 Chapter 21 termination Analysis 937 Chapter 22 Sample Survey On Website attachment A References and Bibliography 976 accessory B put overs 978 accessory C Summation Notation 1005 vermiform appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 1007 appendage E Using pass Functions 1062 appendix F cipher p- encourages Using Minitab and Excel 1067 Index 1071 This page intentionally left blank confine Preface xxv About the Authors xxix Chapter 1 entropy and Statistics 1 Statistics in use 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 matt and Quantitative Data 7 Cross-Sectional and clock 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 Inferenc e 15 1. 6 Computers and Statistical Analysis 17 1. 7 Data Mining 17 1. 8 estimable Guidelines for Statistical Practice 18 heavy define 20 gloss 20 Supplementary Exercises 21 auxiliary 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 portion Frequency Distributions 34 Bar charts and Pie Charts 34 x Contents 2. Summarizing Quantitative Data 39 Frequency Distribution 39 Relative Frequency and per centum Frequency Distributions 41 Dot Plot 41 Histogram 41 Cumulative Distributions 43 Ogive 44 2. 3 Exploratory Data Analysis The Stem-and-Leaf demonstrate 48 2. 4 Crosstabulations and Scatter Diagrams 53 Crosstabulation 53 Simpsons Paradox 56 Scatter Diagram and impetusline 57 abbreviation 63 Glossary 64 break Formulas 65 Supplementary Exercises 65 gaffe trouble 1 Peli arsehole Stores 71 C ase Problem 2 Motion Picture Industry 72 Appendix 2. 1 Using Minitab for Tabular and Graphical Presentations 73 Appendix 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 weeny Fry Design 86 3. Measures of spatial relation 87 Mean 87 Median 88 Mode 89 Percentiles 90 Quartiles 91 3. 2 Measures of Variability 95 scat 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 Chebyshevs 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 affiliation mingled with Two Variables 115 Covariance 115 Interpretation of the Covariance 117 correlation coefficient Coefficient 119 Interp retation of the Correlation Coefficient 120 3. The Weighted Mean and Working with Grouped Data 124 Weighted Mean 124 Grouped Data 125 Summary 129 Glossary 130 Key Formulas 131 Supplementary Exercises 133 Case Problem 1 Peli burn down 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 raw material Relationships of Probability 164 Complement of an Event 164 Addition Law 165 4. 4 Conditional Pro bability 171 Independent Events 174 Multiplication Law 174 4. Bayes Theorem 178 Tabular nuzzle 182 Summary 184 Glossary 184 xii Contents Key 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 respect and Variance 202 Expected encourage 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 set and Variance for the Binomial Distribution 214 5. Poisson Probability Distribution 218 An Example Involving Time Intervals 218 An Example Involving Length or infinite Intervals 220 5. 6 Hypergeometric Probability Distribution 221 Summary 225 Glossary 225 Key Formulas 226 Supplementary Exercises 227 Appendix 5. 1 Discr ete Probability Distributions with Minitab 230 Appendix 5. 2 Discrete Probability Distributions with Excel 230 Chapter 6 Continuous Probability Distributions 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 computation 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 savor distributio n and try out Distributions 265 Statistics in Practice MeadWestvaco Corporation 266 7. 1 The Electronics Associates exhaust Problem 267 7. Selecting a Sample 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 Sampl ing 297 Cluster Sampling 298 taxonomical Sampling 298 Convenience Sampling 299 Judgment Sampling 299 Summary 300 Glossary 300 Key Formulas 301 xiv Contents Supplementary 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 marge 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 effects 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 terra firma Properties 33 9 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 surmise Tests 348 Statistics in Practice John Morrell & Company 349 9. create Null and Alternative Hypotheses 350 The Alternative venture as a Research meditation 350 The Null Hypothesis as an Assumption to Be Challenged 351 Summary of Forms for Null and Alternative Hypotheses 352 9. 2 lineament 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 ratiocination Making 381 9. 7 Calculating the Probability of Type II Errors 382 9. Determining t he 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 Associates, 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 organization 407 10. 1 Inferences About the going 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 Diff erence Between Two Population Proportions 429 Interval Estimation of p1 p2 429 Hypothesis Tests About p1 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 I ndependence 472 Statistics in Practice United Way 473 12. Goodness of Fit Test A Multinomial Population 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 Va riance 515 Comparing the Variance Estimates The F Test 516 ANOVA Table 518 Computer Results for Analysis of Variance 519 Testing for the par of k Population MeansAn Observational Study 520 13. 3 Multiple Comparison Procedures 524 Fishers LSD 524 Type I Error judge 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 additive 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 Determination 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 6 31 Case Problem 2 U. S. Department of Transportation 632 Case Problem 3 Alumni free 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 Independ ent 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 Cooks 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 drop 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 wi th StatTools 711Chapter 16 Regression Analysis Model Building 712 Statistics in Practice Monsanto Company 713 16. 1 General Linear Model 714 Modeling Curvilinear Relationships 714 Interaction 718 xx Contents Transformations Involving the leechlike 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 terminal 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 w ith 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 maker 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 S moothing 797 Moving Averages 797 Weighted Moving Averages 800 Exponential Smoothing 800 18. 4 Trend Projection 807 Linear Trend Regression 807 Holts 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 H ypothesis 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- seawallis 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 pass judgment a Lot 924 Selecting 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 cleverness 22-2 22. 1 Ter minology Used in Sample Surveys 22-2 22. 2 Types of Surveys and Sampling Methods 22-3 22. Survey Errors 22-5 Nonsample distribution Error 22-5 Sampling Error 22-5 22. 4 Simple Random Sampling 22-6 Population Mean 22-6 Population core 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 106 2 Appendix F Computing p-Values Using Minitab and Excel 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 economicals, 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 entropy summary and statistical methodology are an implicit in(p) 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 go out sound methodological development and to use notation that is generally accepted for the topic being cover. Hence, students lead find that th is 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 prefaces the student to the software packages of Minitab 15 and Microsoft Office Excel 2007 and emphasizes the role of calculating machine 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 snitch 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 mod ifications for this newfound edition, we have well-kept the presentation style and readability of those editions. The significant changes in the new edition are summarized here. Content Revisions Revised Chapter 18 Time Series Analysis and Forecasting. The chapter has been exclusively rewritten to cerebrate more on using the pattern in a time series plot to select an catch call 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 reasonables and exponential smoothing. Section 18. 4 introduces methods appropriate for a time series that exhibits a trend. Here we illustrate how degeneration analysis and Holts linear exponential smoothing can be used for linear trend projection, and 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 var iables can be used to model seasonality in a forecasting 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 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 running game 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 distribution of the test statis tic 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 information sets are now available to facilitate computer solution of the exercises. StatTools Add-In for Excel. Excel 2007 does not contain statistical functions or information 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 transfer 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 watchful to make the use 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 leave alone be able to continue to use in the workplace. Change in Terminology for Data.In the previous edition, nominal and ordinal entropy were classified as soft interval and ratio selective information 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 prick 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 as well describ e how the fields of statistics 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 write ups 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 tell, 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 expand to include relatively quick and easy comparisons of two or more data sets. Typical starting salary data for accounting, finance, heed, 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 fr ame. The distinction amidst 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 another Exc el add-in developed by Palisades Corporation that is very assistanceful 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 event of case problems to 31.A new case problem on descriptive statistics appears in Chapter 3 and a new case problem on hypothesis examination 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 dunnhumby 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 as well used by The Wall Street Journal, USA Today, Barrons, 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 appeared in previous editions. Important ones for students are noted here. Methods Exercises and Applications Exercises The end-of-section exercises are fragmentise into two parts, Methods and Applications. The Methods exercises require students to use the formulas and make the incumbent computations. The Applications exercises require students to use the chapter material in real-world situations. Thus, students first focus on the computational nuts and bolts and then move on to the subtleties of statistical application and interpretation. Self-Test ExercisesCertain exercises are identified as 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 reason of the concepts presented in the chapter. Margin Annotations and Notes and Comments Margin annotations that highlight key points and provide additional insights for the st udent 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. twenty-eight 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 C incinnati, 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 stabilising input and suggestions for new case problems, exercises, and Statistics in Practice articles. We would too 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 counseling and support during the preparation of this text. David R. Anderson Dennis J. Sweeney Thomas A. Williams About the Aut hors 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, normality 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 trading 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 Colleges 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. Professor An derson 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 197879, Professor Sweeney worked in the management science group at Procter & Gamble during 198182, 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 management science and statisti cs. The National attainment 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 engineering school 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 the undergradu ate 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 entropy Elements, Variables, and Observations Scales of Measurement Categorical and Quantitative Data Cross-Sectional and T ime Series Data 1. DATA SOURCES Existing Sources Statistical Studies Data Acquisition Errors DESCRIPTIVE STATISTICS STATISTICAL INFERENCE COMPUTERS AND STATISTICAL ANALYSIS DATA MINING good 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 mod YORK, NEW YORK BUSINESSWEEK* With a orbiculate 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 figure 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 conditions. Most egresss 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 su rvey 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 probable 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 data ava ilable 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 understand 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. modish 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 attendant collapse (USA Today, February 13, 2009). The national average price for regular g asoline 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 itemises 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 delimit as the art and science of collecting, analyzing, presenting, and interpreting data. Particularly in business and economics, the informat ion 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 stati stics 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 todays global business and economic environment, anyone can access vast measures 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 clients.For instance, suppose an accounting firm wants to determine whether the amount of accounts due shown on a clients 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. aft(prenominal) reviewing the accuracy of the sampled accounts, the auditors draw a conclusion as to whether the accounts receivable amount shown on the clients 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 averages, a financial analyst can begin to draw a conclusion a s to whether an individual stock is over- or underpriced. For example, Barrons (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 syndicate 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 Todays 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 sample of containers and computes the average number of ounces in the sample. This average, or x-bar value, is plot on an x-bar chart. A plot value above the charts upper control limit indicates overfilling, and a plotted value below the charts lower control limit indicates underfilling. The process is termed in control and allowed to continue as long as the plotted x-bar values fall between the charts 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 indicators 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 computer memorys500 for 2008. Morningstar is a company that tracks over 7000 mutual funds and prepares 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 24. 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 Vanguard 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 f ile 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 (American 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 Measure ment 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 family 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 database, we might use a numeric code by letting 1 designate Domestic Equity, 2 deno

Introduction to Contemporary Society Essay

STUDENT flesh outACAP Student IDNameCourse BASSIX.ASSESSMENT DETAILSUnit/Module Introduction to Contemporary callerEducatorAssessment Name Assignment1Assessment Number 1Term & YearWord sum up 2,121.DECLARATIONI declare that this assessment is my own work, based on my own personal research/study. I excessively declare that this assessment, nor parts of it, has not been previously submitted for any other unit/module or course, and that I have not copied in part or whole or differently plagiarised the work of another student and/or persons. I have read the ACAP Student Plagiarism and Academic Misconduct Policy and understand its implications.Society is a human construct that in its nigh basic form refers to a mathematical group of people who share a sense of community and watch on how to behave within the community so it buttocks function effectively. Socialisation is the process of learning, and adapting as a result of the learning, in identify to successfullyintegrate into s ociety. Although we believe, or wish to believe, that we make choices autonomously, free from the influences of others, this is not the case. Agents of kindisation are those people and groups within a society that influence our self- imagination, which in crease manifests in our attitudes, beliefs, sets and behaviours. Although a myriad of agents contribute to this process over an mortal lifetime the influence and impact these agents have will vary from soulfulness to individual. This essay reflects on three agents of secondary socialisation field identity, the workplace and social media and their influences on my socialisation process.Van Krieken, Habibis, Smith, Hutchins, Marton and Maton (2010) state that national identity is approximately identifying ourselves and others as a collective rather than as individuals a collective that shares a common outlook shaped by either culture, lifestyle or ancestry or all three. National identity, often unconsciously, shapes our dai ly lives as it manifests in our beliefs, values, behaviours, views, words, lifestyle and choices. I am an Australian by choice, having lived in Australia for a number of years and attaining citizenship in June 2006, and a new Zealander by birth. Although both national identities are available to me I identify most sozzledly with my kingdom of birth so I classify myself a new-fashi geniusd Zealander when asked about my nationality.Mori are the Indigenous peoples of young Zealand whilst New Zealanders of European descent can be categorised in several ways Pkeh from the Mori language, which literally trans late(a)s to stranger, New Zealander or colloquially as Kiwis. The Kiwi is a flightless bird unique to New Zealand and is in addition one of its most recognizable national symbols. Of the three terms available to me as a non-indigenous New Zealander I use the term Kiwi as it also encapsulates symbolic aspects of New Zealand. There are a number of key instanceistics that inten d the national character and identity of New Zealanders according to research undertaken by Sibley, Hoverd and Liu (2011) where people who were born in New Zealand were asked what qualities classify someone a true New Zealander. The top 5 characteristics to emerge from this research were liberal democratic values, cultural/bi-cultural awareness, rugby/sporting culture, citizenship and ancestry and flag-waving(prenominal) values, with each characteristic also universedeconstructed into number of related elements.Liberal democratic values, which encompassed pro-social, pro-environmental elements such as friendliness, respect for people and environment, tolerance, equality and work ethic was the characteristic that I believe has most influenced my secondary socialisation and continues to do so on a daily basis. New Zealanders view themselves as egalitarian and classless and this aspect of the national character has a significant influence on me as I place no value on titles, ranks, g ender or backgrounds and my underpinning belief is that everyone is equal disregardless of their wealth, power, race or gender. This may also prove to be a blind spot in my socialisation, as others may perceive my interactions with them as universe disrespectful due to my egalitarian viewpoint.The pro-environment outlook that I have also emanates from my sense of national identity rather than from my family of origin, as I am the simply member of my family that has a green outlook and respects the environment through all forms of recycling. As a child in primary school I was exposed to Mori folklore, which explains the origins of everything from a mythological perspective. As a result of this the attitude of many New Zealanders towards the environment is heavily influenced by the Mori concept of the mauri, or environmental life force, which says that any negative impact on the mauri adversely impacts its energy, which has a negative flow on effect to the lives of people and the e nvironment. This value also underpins my support of the New Zealand Nuclear Free Zone, Disarmament, and Arms Control Act that was passed by the Parliament in 1987, which bans visits by vessels that are nuclear power or armed. From my perspective this piece of legislation also embodies another characteristic of New Zealanders, that of punching to a higher place our weight or fighting for what we believe in socially despite our size.The majority of New Zealanders saw the passing of the legislation as a small nation courageously taking a clear repose on a contentious topic on the realism stage. Cultural/Bicultural awareness is another key characteristic identified by Sibley et.al. (2011) that has influenced me. In the late 1980s there was a spiritual rebirth of Moritanga or Mori culture and a subsequent acceptance of it by the broader New Zealand. This Mori renaissance manifested in a multitude of ways suchas a greater emphasis on Mori cultural expression in the arts, language and tikanga or customs and traditions. Te Reo, the Mori language, was formally recognised as an official language of New Zealand and all Government departments formally incorporated the Mori translation into their names.Even though I no longer reside in New Zealand this still has an influence on my language as I often subconsciously select Mori words or phrases that more effectively explain symbolic concepts than English, which causes a lack of understanding from non New Zealanders. New Zealand art, which incorporates a significant amount of national symbolism and Mori culture, also adorns my home. This assignment has encouraged me to question why I am a patriotic Australian but fiercely patriotic New Zealander. Upon reflection it is my belief that it was my mother, an agent of primary socialisation and a fiercely patriotic woman, who instilled in me the strong sense of national pride that I still possess today. She reminded me often how fortunate I was to be a Kiwi and to never forget that we were booming to live in Gods own country, a phrase used proudly by New Zealanders since the late 1880s to typify New Zealand.The primary school I attended also served to deepen this sense of patriotism, as we sang the national anthem at our weekly assembly, which merely served to deepen the connection I felt. Whilst the value I place on work is determined primarily by my familys values it also has relate to national identity. In the research undertaken by Sibly et. al. (2011), which snapes on defining the national identity of New Zealanders, many New Zealanders reported that working hard and attempt to get ahead, colloquially referred to as the number 8 fencing wire mentality, were national traits, a philosophy that I was brought up to believe in. Work is also an important aspect of my life for more than economic reasons. It is a significant contributor to my identity as it allows me to be viewed as an individual rather than by my relationships with others such as being someones partner.From an economic perspective, I place significant value on being self-sufficient as a result of a my upbringing and this, combined with the value I place on achievement, has driven me to consciously progress my life story in order to visualise that I can remain independent financially. An output of these motifs is that I am more comfortable in the role of the primary, rather than secondary, breadwinner in my intimate relationships. Meisenbach (2010) undertook research on the phenomenological experiences of women who were the primary income earners in their relationships, either through choice or circumstance, seeking to determine elements of commonality from these experiences. Six key themes emerged from the womens experiences the need for control, valuing independence, feelings of stress, placing value on a partners contribution, feelings of resentment or guilt, and placing value on progressing their careers.Although there was variation around the value the par ticipants placed on each element, most agreed that the financial independence the role of female person breadwinner gave them formed an important aspect of their identity. The value placed on this aspect was normally attributed to a parent actively encouraging their independence, or to a negative example they saw whilst growing up, so the status of main breadwinner in their lives ensured a sense of independence they felt would be a positive factor for negotiating any snarly times in their lives. This resonated with me as my mother was ill educated and as a result financially dependent on my stepfather so she stayed in a non-supportive relationship, which had significant impact on me. As noted above another essential element was that the majority of participants identified as being ambitious and career driven, in many cases, much more so than their male partners.This is another aspect that resonates with me as it is my belief that one of the major reasons I am constantly studying i s that continuing education is a basis for progressing my career thereby as a means of maintaining my ability to remain independent financially if the need were to arise, through either choice or circumstance. This unwavering focus on remaining financially independent, even within a committed and loving partnership, must be handled with care in order to ensure my hubby understands that that my need to be in control financially in no way no way diminishes his financial contribution or status. Social media also has growing impact on my socialisation, both personally and professionally. I experience a conflicted relationship with it. From a positive perspective I use social media as a mechanism to keep in constant touch with family overseas.On thenegative aspect of social media, I find the intrusiveness of it frustrating as some individuals seem to feel the need to be on social media constantly despite being physically in my company. I am also perplexed at the self-focused culture so cial media is breeding in our young people where they seem to record life rather than experience it. I have a different relationship with social media from a professional perspective. I use LinkedIn a meshing and job search tool so I connect with people Ive previously worked with, join groups of other like-minded professionals, use it as a passive job search mechanism by having an online resume posted. Hemel (2013) says In the past year LinkedIn has emerged as one of the most powerful business tools on the planet. Long considered a repository for digital rsums, the network now reports 225 million members who have set up profiles and uploaded their education and job histories (pg 68).She also goes on to say that people are victimisation LinkedIn for a multitude of purposes such as building professional portfolios of their work, recommending colleagues and keeping abreast of trends by reading LinkedIn Today, which has news from a myriad of sources including key LinkedIn influencers. With tough economic conditions prevailing over the last few years I made the decision to leave self-employment after ten years to return to the perceived stability of full time, paid employment in order to retain my financial independence. During this time I used LinkedIn to monitor trends in employment, update my network in anticipation of the change, peruse online job opportunities posted on LinkedIn, connect with recruiters, who are prevalent on LinkedIn nowadays, and to post an updated resume and career history.I also used it to investigate and research companies prior to attending interviews. Social media also has a role to play in maintaining links with my national identity. Expatriation is a major phenomenon according to the New Zealand Government statistics as 16% of New Zealanders and 25% of overall tertiary educated New Zealanders live overseas with the largest group residing in Australia. In 2004 Kiwi Expatriates Abroad (K.E.A) was formed to connect expatriate New Zealan ders to their nation, to promote New Zealand to the world and to enhance business opportunities via an online presence on Facebook, LinkedIn and the Internet. I joined at the outset and have used the group to network in order to createbusiness opportunities. With a membership of over 100,00 people, K.E.A demonstrates that national identity doesnt always mean residing in a county in order to identify with it, as the use of social media now makes the worlds boundaries less relevant.Agents of socialisation do not exist as static entities that have a defined, once off influence on an individuals identity at a given point in time but rather as dynamic entities that continue to interact with one another throughout an individuals lifetime. These agents also vary in their influence and impact on individuals and they encourage individuals to learn and adapt in order to fit comfortably into society.ReferencesHempel, J. (2013). LinkedIn How its Changing Business(And How To Make It work For You ). Fortune. 168(1), 68-1NULL.Meisenback, R. J. (2010). The Female BreadwinnerPhenomological Experience and Gendered Identity in Work/Family Spaces, Sex Roles 62(1/2), 2-19. Doi10.1007/s11199-9714-5.Sibley, C.H., Hoverd, W.J, & Liu, J.H. (2011). pluralistic and Monocultural Facets of New Zealand National Character and Identity. New Zealand Journal of Psychology, 40(3)19-28).Te Ara The Encyclopaedia of New Zealand. (2013). National Identity. Retrieved from http//www.teara.govt.nz/en/new-zealand-identity/page-6van Krieken, R., Habibis, D., Smith, P., Hutchins, B., Martin, G. & Maton, K. (2010). Sociology. (4th ed.). Sydney Pearson Australia.

Marilyn Monroe Biography

What began as a brunette beauty by the name of Norma Jeane Baker born in LA, atomic number 20, soon transformed into the towheadedst, biggest sex symbol Hollywood has yet to cross paths with. Marilyn Monroe belonged to the public from the moment she stepped onto the screen and the voluptuous, 50s goddess knew it. In combination with Monroes charming in-person life and alluring pout and sensuality, the bombshell left an ever-staying impact on Hollywood, subdued yet to be outshined by any sex icons to come. baptize Norma Jeane Baker, the soon to be star allow, spent most of her childhood migrating through foster homes after her mother, Gladys Baker, was instituted and her fathers identity remained unidentified. This was up until Norma Jeane was taken under the administer of a family friend, Grace McKee Goddard. Unfortunately, after Mr. Goddard was transferred to the east coast, the family could not afford to travel 16-year-old Norma Jeane along with them. When faced with choice to return to the orphanage or get espouse, Norma married her 21-year-old neighbor Jimmy Dougherty.They were wed in 1942 after dating for six months prior. It was smooth sailing until 1944 when he was transferred to the South Pacific with the marines. During his absence, Norma Jeane took a job on the assembly line at Radio Planes Munitions factory where she was discovered by photographer David Conover. Conover came across the photographers dream as put by David, while taking pictures of women contributing to the war effort for Yank magazine. Conover began her career by sending her modeling jobs and within two years she was a reputable model with plenty of covers to her credit.In 1946, Norma Jean divorced her husband Jimmy and signed with Twentieth Century Fox. It was at this point that she officially transformed into the blonde Hollywood babe known as Marilyn Monroe. Two marriages later, iodin to baseball coquetteer Joe DiMaggio and the third and final to playwright Arthur Miller, Marilyn Monroe was found dead in her Brentwood, California home on August 5, 1962. Although only age 36 when she died, Marilyn Monroe was a global sensation in her lifetime and will always remain that way. oer Marilyn Monroes career span, the icon was nominated and won ountless awards. Among these, Monroe won the Golden Plate at the David di Donatello Awards for her acting in The Prince and the Showgirl, two Henrietta Awards for World Film Favorite-Female and a Golden Globe for Best Motion Picture Musical/Comedy for Some Like it Hot. She also won Most Popular Female leading and a special award at the Photoplay Awards and received a motion picture star on the Hollywood Walk of Fame. Along with these prestigious awards were her many a(prenominal), many nominations which she always came close in.Marilyn began her acting career with 1947s The Shocking Miss Pilgrim but it was her performance in the 1953s Niagara that grabbed the publics eye. She was also one of the most photographed p eople in the world and has been recaptured in numerous paintings. Many of the best in visual art has had a shot at Marilyn Monroe. flush after her death, the bleach blonde has been subject to some 600 books, newspaper and magazine articles, musicals, a ballet, plays, an opera and a famous Elton John/Bernie Taupin song. All of this proves the impact of Marilyn Monroe and average how much the public still loves her.None can deny the influence Marilyn Monroe had on Hollywood and the public alike. Marilyn Monroes influence in Hollywood and on the public can be seen everywhere. During her lifetime it was exemplified through her numerous box-office successes and massive publicity. Monroe did much to make her influence everlasting while the provocative super starlet still graced the world with her presence. She notoriously negotiated a tough contract with her studio involving her artistic rights and was the first woman to set up her own production company.There was intelligibly more to Ms. Monroe than her seductive pout. Marilyn launched her close friend, Ella Fitzgeralds career when she made a deal with the owners of a popular club that refused Ella the opportunity to perform due to her race. Monroe told the owners that if they were to let Ella perform, Marilyn herself would be at every performance. Marilyn broke down the norms for typical actresses and women in general within society. Marilyn was one of the first women to be overly sexual and play sweet, naive and innocent characters at the same time. as put by Time Magazine. This was only one barrier Monroes influence knocked down. She was a astray accepted and adored sex symbol, making the 1950s society more permissive of sex. This also contributed to the sexual freedom women have today.The Edmonton Journal in Canada wrote astir(predicate) the popular face piercing titled The Monroe after the world famous sex symbol. As written in the journal, Its a testament to Marilyn Monroes enduring coolness that the pie rcing is named about her, because her real mole was on her left cheek. Part of the reason why Monroes influence is so satisfying is due to the manner that she came and left. After almost every actress prior to the 50s being brunette and vampy or blonde and entirely lackluster and innocent, Marilyn was a breath of fresh air on the cinema screen. On the other hand, her departure seemed to showcase the wonder and tragedy of Hollywood and the entertainment business. (Glatzer,33). By end at such a young age, Marilyn Monroe became a legend, ergo her gripping influence.It can be understood that the individuals who are remembered and beloved are the tragic heroes and heroines who loosen their lives in Hollywood. (Glatzer,33). Among these are Kurt Cobain, Elvis Presley, James Dean and of course Marilyn Monroe. They didnt have the opportunity to bow out gracefully, they were ripped from our midst and society mourns them all the more. (Glatzer,35). Marilyn Monroe was doubtless the most fam ous and iconic actress of her generation. Many voluptuous blondes attempted to reinvent Monroe after her death but none had the right alchemy of va-va-voom and virtue.In a recent survey, Marilyn Monroe was ranked seventh in The Highest Paid Dead Celebrities and was the only woman in the top thirteen. In combination with Monroes fascinating personal life and alluring sensuality, the bombshell left an ever-staying impact on Hollywood, still yet to be outshined by any sex icons to come. Marilyn Monroe will never be forgotten, whether its her scandalous life or her incredible impact, we all just cant seem to get that enticing pout out of our minds.