Evaluation of Deterrence Theory Research Paper Example | Topics and Well Written Essays - 1500 words

Evaluation of Deterrence Theory - Research Paper Example In my evaluation, I use the evaluation method proposed by Akers and Seller. In this technique, the theory is evaluated using its scope, logical consistency, parsimony, testability, empirical validity, and its usefulness and policy implication. A major advantage of this method that it can give us the chance to evaluate almost all the aspects of this theory. Theory Discussion This theory uses the idea that fear of punishment or negative consequences resulting from committing a crime can cause individuals to refrain from committing offenses (Maimon, 2012). One of the things this theory uses in explaining criminology is human rationality. It says that human nature is motivated to do something that has more gains than losses. Therefore, if someone sees that he will have more loss than gain from a crime when he is caught, he will be motivated to refrain from the crime. This theory thus proposes that severe punishments should be imposed on crimes and offenses to increase the risks that a pe rson exposes himself to when committing them. The theory also uses the concept of an individual’s free will and the power of a person to make calculated choices in explaining crime. This theory states that people commit crimes due to the drive to do so from their free will without being directed to do so by someone else. However, it indicates that in making a choice to commit a crime individuals to analyze the gains and losses which might result from the choices they want to make. As a result, the choices they make are always calculated to make sure they maximize gains while minimizing risks. If severe punishments are imposed on crimes they will make the crimes to be less attractive and hence make individuals refrain from them. This theory explains individual offending and how people can be deterred from committing crimes. It suggests that imposing formal legal punishments can deter individuals from offending. However, according to Maimone al (2012), the theory explains that the deterrent effect of these formal legal punishments depends on their severity, certainty, and celebrity.

Made in Chelsea analysis of an episode Essay Example for Free

Made in Chelsea analysis of an episode Essay From the episode of Made In Chelsea I watched, I can say that the representations we have of upwardly mobile young city dwellers are that they are social-oriented, whose lives seem to be some care-free that they can cavort around various places in London—and the world—without any problems. We also only see characters of a certain age range—none are, we assume, above the age of thirty—of which the majority have no jobs or business, leading us to believe that they come from families of ‘old money’, and so having a job themselves would seem rather pointless. Saying that, there are a few characters who do possess their own business or thereabouts. However, our perceptions of the characters are very one sided, as we are constricted to seeing only one side of that character—the one that fits their current storyline the best. This prevents us from seeing, per se, the kind heartedness of a character that has just cheated on their partner. The words ‘characters’ and ‘storylines’ fit well with my next point; the conversations and the events that take place throughout the episode seem far too rehearsed and coincidental for them to be actual ‘reality’. Location shots are used of London sights and attractions to establish the setting of the scene. They also are only of Central London attractions, and the shops and restaurants et al all seem to highlight the wealth of the individuals who shop there, eat there etc. Reactions, for the majority of the show, are shown using over-the-shoulder shots to portray the reaction of the person who is being told something. There is also usage of eye line matching shots that show you what the character may have been looking at from their angle. The episode seems to comprise of short segments that have then been edited in post production so that they can seek out the most entertaining of segments. This is obvious as the episode transitions from one group of people at a restaurant to a boxing arena and then back to the restaurant again. Tzvetan Torodov’s narrative theory that conventional narratives are structured into five stages; Equilibrium—disruption—recognition—repair—reinstatement, could be present within the episode, as you can apply it to the situation between Louis, Spencer and Jamie (the love triangle storyline). The fact that it fits so well with Torodov’s theory does support the question â€Å"How much of Made In Chelsea is actually reality? †

The Youth Offenders Program :: essays research papers

The Youth Offenders Program   Ã‚  Ã‚  Ã‚  Ã‚  To be honest, I was really pissed off that I had to enter the Zona Seca program to begin with. My so-called infraction was a simple case of being in the wrong place at the wrong time. I am a full time student who works at least twenty-eight hours a week and is extremely pressed for time. The commute from Los Angeles was an extreme inconvenience. Just had to get that off my chest. Do not be fooled, I am extremely grateful for the opportunity to attend this program. I just wish I could have took it here in L.A Surprisingly enough, the Zona Seca program was nothing like I expected. Going into the program I expected lengthy and boring lectures by condescending bureaucrats. To my surprise, the classes were interesting and informative. Our instructors both at the Rehabilitation Institute and the Zona Seca office were very understanding. More programs that are prevention orientated rather than reactionary like Zona Seca are needed. Before the first class session I viewed Zona Seca as a kind of punishment; afterwards more like a therapy/counseling session. The visit with the coroner really struck a nerve. When the coroner started talking about the way young adults drink alcohol as opposed to the way most adults do I could not help but think of all the times I have gotten belligerent. He made the statement that most young people drink to get to drunk. I could not agree more. Although I do drink because I like the taste of alcohol, that taste was definitely acquired. When I first started drinking it was for the sole purpose of getting drunk. Death as a result of to much alcohol was something I was completely oblivious to. Imagining how close to permanent unconsciousness I may have been is extremely scary. I can remember being so drunk in Rosa Rito Mexico that I woke up the next morning not remembering a damn thing from the night before. That includes puking up my dinner, the seven hundred and fifty-ml bottle of Bacardi Limon and the ten or fifteen other mixed drinks I had. If my friends did not tell me of the details from the previous night I would had never known what happened. The coroner’s report really made me look at the way I drink. I’m not going to stop drinking, but I am going to be a lot more responsible and careful when I do.

Statistics for Business and Economics

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

Rang de Basanti Notes

* Rang de Basanti * â€Å"You Pakistani† scene. Police bribed. Extreme nationalism – no western music, dancing, etc. * Mother figure, mother India * Around 30 mins, say what is there to be patriotic about? Corrpution, population, etc. But fighter pilot says no country is perfect, I’m still willing to give my life for my country. * Karan – college kid, pressure from father. Every second someone is born in this country, no one cares about them. Neither government nor God. Do something or else you will be one of them. â€Å"SMS generation† * Don’t take film seriously at first, make fun of the language.Different times, say they can’t relate. * Aslam – Muslim, with Hindu friends. Family portrayed as violent, hating Hindus because Muslims are not accepted in India. * Sue is disappointed that India is not the romantic country she thought it was? * They had found new heroes, and we had no one to blame but ourselves. We were hearing the echoes of our own guns. Scene of the Amritsar massacre. Men, women, children fired upon by Dyer’s armyas they tried to escape, women with babies jumping in wells. Boys say we are like ants, taking everything lying down/not reacting.Mom says this generation lacks will to do anything – someone part of the massacre went all the way to London to kill Dyer. Punjab families send at least one son to army, sacrifice runs in blood – militant portrayal of Punjabs? * Friends scene – saying maybe Ashfaq should go to Afghanistan, he will be safe with â€Å"his own†. Friend asks why am I not one of your own? First friend asks for forgiveness, it is as much your country as it is mine. * Not terrorists, revolutionaries. Tortured, but did not break. McKinley had a problem with the torture, Bismil said it is not your fault – you are just doing your duty. Went to Ashfaq, said Bismil will create a country for Hindus. He said no, this is for the freedom on Hindu stan, but you wouldn’t understand because you’ve been a slave to that kind of thought for so long. * Did revolutionaries give their lives for nothing? One leg in future, one leg in past. Why don’t you do something to change it? Difference is how you go to the grave. * A woman’s place is at her husband’s feet – laughed at. * Drastic measures. Takes a loud noise to open deaf ears. Hunger strikes in prison. * He got his 21 gun salute at his funeral.But was it in vain? Died with his country’s flag. Saved many lives by not crashing it into the city. Corruption scandal. * Laxman’s realization might mirror what young nationalists found at that time? Saw leader of his movement doing nothing, when they supposedly fought for India. Innocent people were getting hurt aran has finally found his cause. â€Å"waking up† * Colonial legacies – left one behind for another? Cycle. * Class struggles – Sukhi says Karan’s father will just bail him out. Accuses him of knowing that his father was corrupt. * Moral superiority.Bond over common cause, all rivals/problems are overcome * Change from within * Divisive/polarizing figure – some say don’t take the law into your own hands, others praise as the right thing to do when politicians control the law * Revolutionary vs. terrorists * Why didn’t the boys join politics, army, police, etc. to change the way that Ajay said to? * 1: people who go to their grave screaming. 2: people who die without a sound. Third kind of people he came across as being the ones who embraced death as a friend and an equal, with a heartfelt laughter

clarence laughlin essays

clarence laughlin essays Clarence John Laughlin was born in 1905 in Lake Charles, Louisiana. He lived on a plantation near New Iberia. He attended high school for one year in 1918 due to the death of his father. He then worked at many jobs from 1924 to 1935. Laughlins interests were with the writings of Baudelaire, Rimbaud, and the French Symbolists. They inspired him to write poems and stories. In 1934 he began to take photographs. His first one-man show was held, in 1936, at the Isaac Delgado Museum, New Orleans. Laughlin spent one year taking fashion photographs for Vogue magazine. He specialized in color photography during World War II. Since 1946, Laughlin worked as a freelance photographer of contemporary architecture. He published his photographs in a book called Ghosts Along the Mississippi in 1948. Following this, he lectured and had many publications and exhibitions displaying his work. From about 1970 on Laughlin concentrated on writing about his photographs and the world of fantasy. He died in 1985. Laughlin went through a great many style changes in his photographs. Only a few will be looked at and discussed. During his early career, he focused on taking pictures involving glass. He was fascinated with glass because it acts so variably and subtly with light: offers so many suggestions that so-called reality is not the simple thing we usually conceive it to be: that reality embodies many planes and many kinds of meanings. Laughlin believed that it gave off a magic quality. He also was drawn to taking pictures of the old, desolate and worn down buildings of New Orleans. Laughlin felt that these buildings, due to their appearance, were lost in time. He treated them as psychological and poetic documents and not as ordinary historical pieces of architecture. He brought them meaning. During his mid career he began to perform color experiments. Laughlin believed that t...

Bushfires - Burning for the better

Bushfires - Burning for the better Lachlan Bryant Biology Mrs DaviesBURNING FOR THE BETTERBushfires have played a vital role in the up keeping of the Australian bush for millions of years. Much of our vegetation has evolved with fire. Like the vegetation in other harsh and dry environments, it has developed characteristics that promote the spread of fire and in some cases, fire is essential in the reproduction of some native flora (CSIRO). In the Bunya Mountains, recent study has shown that lack of fire has dramatically accelerated the decrease of the number and size of grasslands in the Bunya Mountains which are called balds (NLWRA). This brings to question why there is such an opposition to controlled burning of areas such as the Bunya Mountains when in fact not putting to use controlled burns potentially has a far worse effect than burning.Fire has intense effects on the abiotic factors of forest ecosystems.English: Loggers at their camp in the Bunya Mounta...Surface temperatures have been reported to reach 1,000 °C (Ahlgren) and a number of physicochemical properties of the soil are affected. Severe heating of soil breaks down the structures of the inorganic parent materials, causing the soil structure to become unstable (Ulery). Fire creates layers within the soil that are resistant to water which decreases water infiltration and increasing soil erosion by water runoff (Amlendros, DeBano).The effects on ammonium and nitrate concentrations are variable (Covington, Jorgensen, Kovaci), while concentrations of phosphorus, potassium, and magnesium are reported to increase (D.W Smith).Due to the release of basic cations during combustion and their deposition on the surface of the soil, most studies declare an increase in soil PH after fires (Pietik ¤inen, T.H Anderson).Natural forests and their ecosystems in Australia have evolved to use available rainfall in a way that allows them to survive. In each climate niche, naturally and man...

Animal Farm Summary

Animal Farm Summary George Orwells Animal Farm is an allegorical novel about a group of farm animals who take over their farm in 1940s England. Through the story of the animals revolution and its aftermath, Orwell assesses the failures of the communist revolution in Russia. Chapters 1-2 The novel opens at Manor Farm, where Mr. Jones, the cruel and incompetent farmer, is drunkenly going to sleep. As soon as the lights in the farmhouse go out, the animals gather. Old Major, an elderly boar whos lived on the farm for a long time, has called a meeting. At the meeting, Old Major describes a dream he had the previous night, in which the animals lived together without humans. He then launches into an impassioned speech. In the speech, he argues that humans are the enemies of all animals, and he urges the animals of the farm to organize and rebel against the humans. Old Major teaches the animals- who have varying degrees of intelligence- a song called Beasts of England in order to instill a sense of revolutionary fervor in them. Old Major passes away three days later. Three pigs named Napoleon, Snowball, and Squealer use this sad event to rally the animals. When the animals, who are starving, break into the store shed, Mr. Jones attempts to whip them. The animals revolt and drive Mr. Jones, his family, and his employees off the farm in terror. Napoleon and Snowball quickly organize the animals and remind them of Old Major’s teachings. They give the farm a new name- Animal Farm- and hold a meeting to vote on rules. Seven fundamental principles are adopted: Whatever goes upon two legs is an enemy.Whatever goes upon four legs, or has wings, is a friend.No animal shall wear clothes.No animal shall sleep in a bed.No animal shall drink alcohol.No animal shall kill any other animal.All animals are equal. Snowball and Napoleon order that these principles of Animalism be painted on the side of the barn in large white letters. The cart-horse, Boxer, is particularly excited and declares that his personal motto will be â€Å"I Will Work Harder.† Napoleon does not join the animals in the harvest, and when they return, the milk has disappeared. Chapters 3-4 Snowball undertakes a project to teach all the animals on the farm how to read and write. Napoleon takes charge of a litter of young puppies in order to teach them the principles of Animalism. He takes the puppies away; the other animals never see them. The animals work together and know the business of the farm very well. For a time, the farm is peaceful and happy. Every Sunday, Snowball and Napoleon gather the animals for a meeting in which they debate what to do next and vote. The pigs are the smartest of the animals, and so they assume leadership and create the agenda every week. Snowball has many ideas for improving the farm and the lives of the animals, but Napoleon is against almost all of his ideas. When the animals complain that they cannot remember so many of Animalism’s commandments, Snowball tells them that all they have to remember is â€Å"Four legs good, two legs bad.† Neighboring farmers are afraid that a similar overthrow could take place on their own farms. They band together with Mr. Jones to attack the farm with a gun. Snowball thinks quickly and organizes the animals into an ambush; they surprise the men and chase them off. The animals celebrate the â€Å"Battle of the Cowshed† and confiscate the gun. They decide to fire the gun once a year to commemorate the battle, and Snowball is hailed as a hero. Chapters 5-6 At the next Sunday meeting, Snowball suggests building a windmill, which will provide electricity as well as grind grain. He makes a passionate speech arguing that the windmill will make their lives easier. Napoleon gives a short speech opposing the matter, but he can tell he has lost the argument. Napoleon makes a sound, and suddenly the dogs he took away for education- now fully grown- burst into the barn, snarling and biting. They chase Snowball away. Napoleon tells the other animals that Snowball was their enemy and had been working with Mr. Jones. He announces that the meetings are no longer necessary, and that Napoleon, Squealer, and the other pigs will run the farm for the benefit of everyone. Napoleon decides to build the windmill after all. Work commences on the windmill- Boxer works especially hard at it, excited at the easier life they will have when it is done. The animals notice that Napoleon and the other pigs begin to act more like men: standing on their hind legs, drinking whiskey, and living inside. Whenever someone points out that this behavior violates the principles of Animalism, Squealer explains why they are wrong. Napoleons leadership becomes increasingly totalitarian. When a storm causes the windmill to collapse, Napoleon deflects blame by telling everyone that Snowball sabotaged it. He corrects the animals about their memory of the Battle of the Cowshed, insisting he was the hero they all remember, and that Snowball was in league with Mr. Jones. He accuses various animals of being in league with Snowball; his dogs attack and kill each one he accuses. Boxer accepts Napoleons rule, repeating â€Å"Napoleon is always right† as a mantra as he works harder and harder. Chapters 7-8 The windmill is rebuilt, but another farmer, Mr. Frederick, gets into a disagreement over a business deal with Napoleon and uses explosives to destroy the new windmill. Another battle ensues between the animals and the men. The men are once again driven away, but Boxer is severely injured. The animals discover Squealer with a can of white paint; they suspect the Animalism principles painted on the barn have been altered. Chapters 9-10 Boxer continues to work, driving himself to do even more despite his injuries. He grows weaker, and eventually collapses. Napoleon tells the animals he will send for a veterinary hospital to come get Boxer, but when the truck arrives, the animals read the words on the truck and realize Boxer is being sent to the ‛knacker’ to be made into glue. Napoleon has sold Boxer for whiskey money. Napoleon and Squealer deny this and claim that the truck had recently been purchased by the hospital and hadn’t been repainted. Later, Napoleon tells the animals that Boxer passed away under a doctor’s care. Time passes. The windmill is rebuilt again and generates a lot of income for the farm, but the lives of the animals get worse. No longer is there talk of heated stalls and electric lights for all. Instead, Napoleon tells the animals that the simpler their lives are, the happier they’ll be. Most of the animals who knew the farm before the revolution are gone. One by one, the principles of Animalism have been erased on the side of the barn, until only one remains: â€Å"All animals are equal, but some animals are more equal than others.† The simplified motto has been changed to â€Å"Four legs good, two legs better.† The pigs have become almost indistinguishable from the men: they live inside, wear clothes, and sleep in beds. Napoleon invites a neighboring farmer to dinner to discuss an alliance, and changes the name of the farm back to Manor Farm. Some of the animals peer into the farmhouse through the windows and cannot tell which are the pigs and which are the men.

Risk Management Essay Example | Topics and Well Written Essays - 1250 words - 1

Risk Management - Essay Example Most Significant Themes Risks Associated with Fiscal Issues Fiscal risks are another area that was significant to me because of the government’s role in provision for public utilities and in ensuring a potential to control the economy. The most significant risk that is associated with fiscal issues is the scarcity of sources of funds for the government. The government borrows money through bonds that it creates but the market for such bonds may be stretched. Additional bonds in the market is for example associated with anticipated increased interest rates and this is a challenge because lack of finances is the reason for floating bonds and the increased interest rates may be too unbearable for the government. Inability to fund an economy’s budget and pay for existing debts further worsen the risk of scarce resources. Debt limit under fiscal policies is another significant risk (Malin n.p.). While existence of debt is a significant destabilizing factor, established statu tory limits create increases levels of uncertainties among stakeholders such as investors and creditors who may identify future economic instability or the government’s inability to repay its existing debts. ... Government’s ability to advance incentives is another potential risk (Malin n.p.). Diversified policy measures however exist to for preventing the risks from occurring and even managing their impacts in case of occurrence. A review of a fiscal scope that focuses on a wider scope than the budget, debt, and analysis of potential risks in a portfolio are examples. Being strict to operate within predetermined limits is another measure to managing potential exploitation in contracts. Further measures such as analysis of principle fiscal risks and debt sustainability vulnerabilities and review of fiscal inefficiencies and probable liabilities are significant to management of fiscal related risks. Analytical approach to impacts of the fiscal risks is another approach to mitigating effects of the risks (Malin n.p.). Risks of debt limits can also be managed through fiscal policy initiatives. The Federal reserve can for example reduce investments in some public funds and concentrate on demanding needs as a strategy to reducing expenditure and the need for more debt. While sale of debts offers opportunities for reducing debt levels, nonmarketable debts may not be successful and their sale should be suspended. The government can also limit auctions on some securities and even reduce some of its expenditures such as social security benefits payments and advances to some creditors and vendors (Malin n.p.). Foreign Exchange Risk The concept of foreign exchange risk is one of the most significant themes that I derived from the course. Its significance emanates from the increasingly globalized environment that ensure cross border interaction among governments and private sector institutions. The interactions are further associated

Using Mobile Technology and Mobile devices in the workplace Essay

Using Mobile Technology and Mobile devices in the workplace - Essay Example In fact, a research carried out by the Foresights Networks and Telecommunications, Q1 2011, indicate that 64 percent of all firms in North America and Europe identify the provision of adequate mobility support for their staff as a top priority (Wright, Mooney, & Parham, 2011). Memorandum To: Leslie Anderson President, SC Technology Company From: Lehua Lashua IT Administrator Subject: Mobile Devices in the Workplace Date: November 14, 2012 Executive summary The benefits that come along with mobile devices and mobile technology usage at work places are many and influence the employee’s accessibility, quality, and ability to make meaningful decisions based on the given information timelines. People use mobile devices in addition to other office tools to mediate the tasks and activities required to fulfill certain responsibilities at work (Brennen, 2011). Thus, it is essential for organizations to apply the use of mobile technology and mobile devices with respect to the need to achieve goals set and objectives allocated by an organization. Today’s world is so technologically vibrant such that in order to attain the set targets and meet every customer’s demand, organizations should find it imperative to adopt and integrate the application of mobile devices and mobile technology.... This report will seek to propose why organizations such as SC Technology Company need to acquire, integrate, and coordinate the use of mobile technology and mobile devices at workplace. Introduction As mobile technology continues to advance and mobile devices become much cheaper and evolve with regard to their portability, interfaces, bandwidth, features, and context awareness, people are constantly making these devices part of their social and professional worlds. The introduction of the so-called â€Å"Smartphone† has irreversibly revolutionized the way people conduct business. Whilst laptops and desktops continued to dominate the last decade and half, the advent of mobile computing technology has become much more novel, changed the presentation even though not necessarily the way people currently do business (Wright, Mooney, & Parham, 2011). Exponentially, the business world has injected new and sophisticated technological devices and other products that are transforming th ey way companies view profit margins, competition, and time. The application and use of mobile devices have erased working boundaries and replaced them with substantially portable, integrated, and accessible gadgets. These devices are suitable for doing away with the distance involved in cases where employees used to take time and resources before realizing the intended objectives (Katz, 2011). Purpose The main of this essay is to prepare a report based on an ongoing research regarding the application and use of mobile devices and mobile technology in workplaces (SC Technology Company). It will also report on how mobile technology and mobile devices can help increase an organization’s productivity and

Assessment Essay Example | Topics and Well Written Essays - 500 words - 8

Assessment - Essay Example guidance and management of the school principal whose participation influences the level of school efficiency through aligning individual teacher instructions with student achievement. My participation in instructional collaboration would be to intensify my relationship with colleagues, offer my opinion towards a student-centered school, gain proficiency in curriculum goals, raise my expectations and that of other teachers, become part of an aggressive and engaged community of teacher-learners, and reinforces the entire school program. I would hesitate to collaborate to avoid over-dependence on my colleagues, and to gain more confidence my decisions. I would also not collaborate to pursue issues geared towards personal goals that conflict with student-centered learning. In my opinion, successful collaboration must improve my reflective abilities and promote individual professional growth. Additionally, partners have to demonstrate strong self-esteem and motivation, sense of security hence a common goal, shared studying and peer observation, open and rich professional dialogues, instructional variety in teaching, elevated risk taking, planning and preparation, and improved of levels self-confidence. Through increased participation in of teachers curriculum delivery, collaboration makes it possible to evaluate the outcomes of both the teachers and the students. Yes, I have previously participated in instructional collaboration. I engaged in consultation collaboration for a topic I did not know how to deliver effectively. The experience made me to realize that not all teachers feel secure when engaging in collaboration and they would turn you away claiming they are busy. However, most teachers are willing to assist newly employed colleagues in effort to promote consistent student learning. I felt incompetent and did not want to jeopardize the learning of my students. I would look for a partner who is trustworthy and who seeks equitable distribution of

Business report Research Paper Example | Topics and Well Written Essays - 1750 words

Business report - Research Paper Example Business report In the present day scenario, the hospitality, leisure and tourism industry of the world is experiencing immense growth owing to augmenting demand from the customers towards the facilities served by this particular industry. The industry is precisely explained as one of the major service industries that offer wide range of services like lodging, restaurant facilities, theme park benefits and transportation facilities among others, to a wide range of customers. Correspondingly, maintaining high level of integrity and delivering due significance towards the level of employee commitment is also crucial in order to obtain overall business efficiency. 1.0. Background Considering the chances witnessed in the modern era, it can be argued that the immense growth of the hospitality and the tourism industry has been stimulated largely due to the increasing number of people getting attracted towards the wide range of services offered by the participating companies. Notably, maint aining a balance between the quality and quantity of services in the hospitality industry often raise to be a noteworthy challenge, which in turn requires effective organisational leadership practices. Implementation of this particular approach is often argued as an important aspect for the long term sustainability of the industry (Laws, 2004). Aim of the Report. The aim of this report will to evaluate the importance of customer service in the hospitality, leisure and tourism industry around the world and the corresponding components that tend to influence the strategic effectives of its practices in this regard. Accordingly, the paper will recommended for the future approach that the participant companies should undertake with regard to providing effective and integrated customer services in this particular industry. 2.0. Discussion Customers are widely considered as the key attribute of any form of business, especially in the present day context, where the performance of the busin ess is entirely dependent on the behaviour of the customers. However, the importance of customers in the hospitality, leisure and tourism industry resides to a higher level owing to the fact that it is a service industry and also due to the reason that customer retention by ensuring maximum customer satisfaction is the key to sustainability in the industry structure. Providing services to the customers and that too in an efficient and dedicated manner is the core success factor for companies operating in this particular industry in today’s contemporary setting (Leland & Bailey, 2006). Customer Services. As described in the above section of the report, customers are the primary driving force in hospitality business in today’s contemporary world. According to the study of Leland & Bailey (2006), customer service is regarded to be a particular approach taken with the intention to ensure better customer satisfaction through effective and integrated service based business o perations. In precise, it is the assistance or the advice any business provides to its targeted customers on the purchase of services offered. Although the mechanism is not limited to the hospitality industry, it shall not be pious to state that the approach has its maximum significance in this particular

2 Questions Assignment Example | Topics and Well Written Essays - 1000 words

2 Questions - Assignment Example Liking - so now that the consumer is fully aware and knowledgeable about the product, he begins to relate the product with itself and establishes a connection. At this stage, the marketer should already know which level consumers feel the product and should draft their ad campaigns accordingly. It is best for the marketer to now evaluate his consumer base as to whether they think the company offers a better product than its competitors. Conviction - the second to the last stage. Now the consumer is hit with strong advertising and reasons to want to purchase the product over the competitors. A conviction must be created within the mind of the client to support the product. So the marketer must push the advertisement in such a way that the consumer will feel that the competitors cannot compete with this particular brand. Purchase - the final step in the buyer readiness - stage. The effective product information dissemination and strong advertising push creates a conviction within the clients mind that gives him the confidence to push through with the purchase of the product. Once the sale occurs within the consumer base of the product, then the marketer can effectively say that he has done his job properly. 2. An appeal is what an advertiser doe sin order to get a consumer to purchase a product or service. There are three types of appeals that are commonly used in advertising by marketers. These appeal types are known as: Ethical Appeal - this is an appeal targeted at at someones or somethings image. The public does not normally come across this type of advertising unless it is election season. This is because politicians use this advertising platform to present themselves in a better light than the other candidates, oftentimes using criticisms to make them more appealing to the voter. Consumers though, normally do not use this kind of dialog unless they are writing to a company to complain

Models of Organized Crime Essay Example | Topics and Well Written Essays - 500 words

Models of Organized Crime - Essay Example Thus, respected members of society: policemen or law officers are bribed and coerced into allowing these individuals to follow through with their plans. There are two models that seek to explain the presence of organized crime in society: the bureaucratic/corporate model and the patrimonial/patron-client model (Abadinsky 2003). This essay seeks to understand the reasons and influence these two models play on organized crime. The bureaucratic model survives on the tandem of efficiency. It is essential for large operations and activities. Thus, the individuals involved in conducting these organized crimes focus on bringing a degree of competence to the system to ensure it functions properly. This system works under Weber's definition of the various elements to an organization (1947). It needs rules, specialized training, division of labor and an authority. Thus the corporate model functions under one leader who is at the top according to the pyramidal system of authority. There is a system of specialized workers who function under this leader. And the authority maintains its power through various laws: vows of silence when communicating with a law officer. Thus, the larger the organization becomes, the more important it becomes to control it through this system of laws and power.