CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION 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 Cumulative probability z z 00 01 02 03 04 05 06 07 08 09 Ϫ3.0 0013 0013 0013 0012 0012 0011 0011 0011 0010 0010 Ϫ2.9 Ϫ2.8 Ϫ2.7 Ϫ2.6 Ϫ2.5 0019 0026 0035 0047 0062 0018 0025 0034 0045 0060 0018 0024 0033 0044 0059 0017 0023 0032 0043 0057 0016 0023 0031 0041 0055 0016 0022 0030 0040 0054 0015 0021 0029 0039 0052 0015 0021 0028 0038 0051 0014 0020 0027 0037 0049 0014 0019 0026 0036 0048 Ϫ2.4 Ϫ2.3 Ϫ2.2 Ϫ2.1 Ϫ2.0 0082 0107 0139 0179 0228 0080 0104 0136 0174 0222 0078 0102 0132 0170 0217 0075 0099 0129 0166 0212 0073 0096 0125 0162 0207 0071 0094 0122 0158 0202 0069 0091 0119 0154 0197 0068 0089 0116 0150 0192 0066 0087 0113 0146 0188 0064 0084 0110 0143 0183 Ϫ1.9 Ϫ1.8 Ϫ1.7 Ϫ1.6 Ϫ1.5 0287 0359 0446 0548 0668 0281 0351 0436 0537 0655 0274 0344 0427 0526 0643 0268 0336 0418 0516 0630 0262 0329 0409 0505 0618 0256 0322 0401 0495 0606 0250 0314 0392 0485 0594 0244 0307 0384 0475 0582 0239 0301 0375 0465 0571 0233 0294 0367 0455 0559 Ϫ1.4 Ϫ1.3 Ϫ1.2 Ϫ1.1 Ϫ1.0 0808 0968 1151 1357 1587 0793 0951 1131 1335 1562 0778 0934 1112 1314 1539 0764 0918 1093 1292 1515 0749 0901 1075 1271 1492 0735 0885 1056 1251 1469 0721 0869 1038 1230 1446 0708 0853 1020 1210 1423 0694 0838 1003 1190 1401 0681 0823 0985 1170 1379 Ϫ.9 Ϫ.8 Ϫ.7 Ϫ.6 Ϫ.5 1841 2119 2420 2743 3085 1814 2090 2389 2709 3050 1788 2061 2358 2676 3015 1762 2033 2327 2643 2981 1736 2005 2296 2611 2946 1711 1977 2266 2578 2912 1685 1949 2236 2546 2877 1660 1922 2206 2514 2843 1635 1894 2177 2483 2810 1611 1867 2148 2451 2776 Ϫ.4 Ϫ.3 Ϫ.2 Ϫ.1 Ϫ.0 3446 3821 4207 4602 5000 3409 3783 4168 4562 4960 3372 3745 4129 4522 4920 3336 3707 4090 4483 4880 3300 3669 4052 4443 4840 3264 3632 4013 4404 4801 3228 3594 3974 4364 4761 3192 3557 3936 4325 4721 3156 3520 3897 4286 4681 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 z z 00 01 02 03 04 05 06 07 08 09 5000 5398 5793 6179 6554 5040 5438 5832 6217 6591 5080 5478 5871 6255 6628 5120 5517 5910 6293 6664 5160 5557 5948 6331 6700 5199 5596 5987 6368 6736 5239 5636 6026 6406 6772 5279 5675 6064 6443 6808 5319 5714 6103 6480 6844 5359 5753 6141 6517 6879 6915 7257 7580 7881 8159 6950 7291 7611 7910 8186 6985 7324 7642 7939 8212 7019 7357 7673 7967 8238 7054 7389 7704 7995 8264 7088 7422 7734 8023 8289 7123 7454 7764 8051 8315 7157 7486 7794 8078 8340 7190 7517 7823 8106 8365 7224 7549 7852 8133 8389 1.0 1.1 1.2 1.3 1.4 8413 8643 8849 9032 9192 8438 8665 8869 9049 9207 8461 8686 8888 9066 9222 8485 8708 8907 9082 9236 8508 8729 8925 9099 9251 8531 8749 8944 9115 9265 8554 8770 8962 9131 9279 8577 8790 8980 9147 9292 8599 8810 8997 9162 9306 8621 8830 9015 9177 9319 1.5 1.6 1.7 1.8 1.9 9332 9452 9554 9641 9713 9345 9463 9564 9649 9719 9357 9474 9573 9656 9726 9370 9484 9582 9664 9732 9382 9495 9591 9671 9738 9394 9505 9599 9678 9744 9406 9515 9608 9686 9750 9418 9525 9616 9693 9756 9429 9535 9625 9699 9761 9441 9545 9633 9706 9767 2.0 2.1 2.2 2.3 2.4 9772 9821 9861 9893 9918 9778 9826 9864 9896 9920 9783 9830 9868 9898 9922 9788 9834 9871 9901 9925 9793 9838 9875 9904 9927 9798 9842 9878 9906 9929 9803 9846 9881 9909 9931 9808 9850 9884 9911 9932 9812 9854 9887 9913 9934 9817 9857 9890 9916 9936 2.5 2.6 2.7 2.8 2.9 9938 9953 9965 9974 9981 9940 9955 9966 9975 9982 9941 9956 9967 9976 9982 9943 9957 9968 9977 9983 9945 9959 9969 9977 9984 9946 9960 9970 9978 9984 9948 9961 9971 9979 9985 9949 9962 9972 9979 9985 9951 9963 9973 9980 9986 9952 9964 9974 9981 9986 3.0 9987 9987 9987 9988 9988 9989 9989 9989 9990 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 © 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 permissionrequest@cengage.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 Production House/Compositor: MPS Limited, A Macmillan Company Student Edition ISBN 10: 0-324-78325-6 Senior Art Director: Stacy Jenkins Shirley Instructor's Edition ISBN 10: 0-538-45149-1 Internal Designer: Michael Stratton/cmiller design Cover Designer: Craig Ramsdell Cover Images: Getty Images/GlowImages Photography Manager: John Hill Instructor's Edition ISBN 13: 978-0-538-45149-9 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 13 12 11 10 09 Copyright.indd 11/16/09 11:43:16 PM Dedicated to Marcia, Cherri, and Robbie This page intentionally left blank Brief Contents Preface xxv About the Authors xxix Chapter Data and Statistics Chapter Descriptive Statistics: Tabular and Graphical Presentations 31 Chapter Descriptive Statistics: Numerical Measures 85 Chapter Introduction to Probability 148 Chapter Discrete Probability Distributions 193 Chapter Continuous Probability Distributions 232 Chapter Sampling and Sampling Distributions 265 Chapter Interval Estimation 308 Chapter 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: Model Building 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 1068 Appendix F Computing p-Values Using Minitab and Excel Step Choose t Step When the t Distribution dialog box appears: Select Cumulative probability Enter 59 in the Degrees of freedom box Select Input Constant Enter 1.84 in the Input Constant box Click OK Minitab provides a cumulative probability of 9646, and hence the lower tail p-value ϭ 9646 The Heathrow Airport example is an upper tail test; the upper tail p-value is Ϫ 9646 ϭ 0354 In the case of a two-tailed test, we would use the minimum of 9646 and 0354 to compute p-value ϭ 2(.0354) ϭ 0708 test statistic We use the St Louis Metro Bus example from Section 11.1 as an illustration; the value of the test statistic is ϭ 28.18 with 23 degrees of freedom The Minitab steps used to compute the cumulative probability corresponding to ϭ 28.18 follow The Step Step Step Step Select the Calc menu Choose Probability Distributions Choose Chi-Square When the Chi-Square Distribution dialog box appears: Select Cumulative probability Enter 23 in the Degrees of freedom box Select Input Constant Enter 28.18 in the Input Constant box Click OK Minitab provides a cumulative probability of 7909, which is the lower tail p-value The upper tail p-value ϭ Ϫ the cumulative probability, or Ϫ 7909 ϭ 2091 The two-tailed pvalue is times the minimum of the lower and upper tail p-values Thus, the two-tailed p-value is 2(.2091) ϭ 4182 The St Louis Metro Bus example involved an upper tail test, so we use p-value ϭ 2091 The F test statistic We use the Dullus County Schools example from Section 11.2 as an illustration; the test statistic is F ϭ 2.40 with 25 numerator degrees of freedom and 15 denominator degrees of freedom The Minitab steps to compute the cumulative probability corresponding to F ϭ 2.40 follow Step Step Step Step Select the Calc menu Choose Probability Distributions Choose F When the F Distribution dialog box appears: Select Cumulative probability Enter 25 in the Numerator degrees of freedom box Enter 15 in the Denominator degrees of freedom box Select Input Constant Enter 2.40 in the Input Constant box Click OK Minitab provides the cumulative probability and hence a lower tail p-value ϭ 9594 The upper tail p-value is Ϫ 9594 ϭ 0406 Because the Dullus County Schools example is a two-tailed test, the minimum of 9594 and 0406 is used to compute p-value ϭ 2(.0406) ϭ 0812 Appendix F 1069 Computing p-Values Using Minitab and Excel Using Excel WEB file p-Value Excel functions and formulas can be used to compute p-values associated with the z, t, 2, and F test statistics We provide a template in the data file entitled p-Value for use in computing these p-values Using the template, it is only necessary to enter the value of the test statistic and, if necessary, the appropriate degrees of freedom Refer to Figure F.1 as we describe how the template is used For users interested in the Excel functions and formulas being used, just click on the appropriate cell in the template The z test statistic We use the Hilltop Coffee lower tail hypothesis test in Section 9.3 as an illustration; the value of the test statistic is z ϭ Ϫ2.67 To use the p-value template for this hypothesis test, simply enter Ϫ2.67 into cell B6 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear For Hilltop Coffee, we would use the lower tail p-value ϭ 0038 in cell B9 For an upper tail test, we would use the p-value in cell B10, and for a two-tailed test we would use the p-value in cell B11 The t test statistic We use the Heathrow Airport example from Section 9.4 as an illustra- tion; the value of the test statistic is t ϭ 1.84 with 59 degrees of freedom To use the p-value template for this hypothesis test, enter 1.84 into cell E6 and enter 59 into cell E7 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear FIGURE F.1 EXCEL WORKSHEET FOR COMPUTING p-VALUES A B Computing p-Values Using the Test Statistic z Enter z > Ϫ2.67 p-value (Lower Tail) 0.0038 10 p-value (Upper Tail) 0.9962 11 p-value (Two Tail) 0.0076 12 13 14 15 16 Using the Test Statistic Chi Square 17 18 Enter Chi Square > 28.18 19 df > 23 20 21 22 p-value (Lower Tail) 0.7909 23 p-value (Upper Tail) 0.2091 24 p-value (Two Tail) 0.4181 C D E Using the Test Statistic t Enter t > df > p-value (Lower Tail) p-value (Upper Tail) p-value (Two Tail) 1.84 59 0.9646 0.0354 0.0708 Using the Test Statistic F Enter F > Numerator df > Denominator df > 2.40 25 15 p-value (Lower Tail) p-value (Upper Tail) p-value (Two Tail) 0.9594 0.0406 0.0812 1070 Appendix F Computing p-Values Using Minitab and Excel The Heathrow Airport example involves an upper tail test, so we would use the upper tail p-value ϭ 0354 provided in cell E10 for the hypothesis test test statistic We use the St Louis Metro Bus example from Section 11.1 as an illustration; the value of the test statistic is ϭ 28.18 with 23 degrees of freedom To use the p-value template for this hypothesis test, enter 28.18 into cell B18 and enter 23 into cell B19 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear The St Louis Metro Bus example involves an upper tail test, so we would use the upper tail p-value ϭ 2091 provided in cell B23 for the hypothesis test The The F test statistic We use the Dullus County Schools example from Section 11.2 as an illustration; the test statistic is F ϭ 2.40 with 25 numerator degrees of freedom and 15 denominator degrees of freedom To use the p-value template for this hypothesis test, enter 2.40 into cell E18, enter 25 into cell E19, and enter 15 into cell E20 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear The Dullus County Schools example involves a two-tailed test, so we would use the two-tailed p-value ϭ 0812 provided in cell E24 for the hypothesis test Index Note: Chapter 22 can be found with the Online Content for this book Index entries found in this chapter are denoted by chapter number 22, hyphen, and page number Page numbers followed by a n indicate a footnote A Acceptable quality level (AQL), 930n2 Acceptance criterion, 924 Acceptance sampling, 922–931 binomial probability function for, 925 computing the probability of accepting a lot, 924–927 KALI, Inc example, 924 selecting plans for, 928–929 Accounting, Addition law, 165–168 Additive decomposition models, 829–830 Adjusted multiple coefficient of determination, 655, 655n1 Aggregate price indexes, 765–767 computing from price relatives, 769–770 Air traffic controller stress test, 531–532 Alliance Data Systems, 561 Alpha to enter, 739–740, 743n1 Alpha to remove, 743n1 Alternative hypothesis, 349 as research hypothesis, 350–351 American Military Standard Table (MIL-STD-105D), 929 American Society for Quality (ASQ), 904 American Statistical Association “Ethical Guidelines for Statistical Practice,” 18–19 Analysis of variance (ANOVA), 508–537, 513n3, 513n4 assumptions for, 510 completely randomized designs and, 513–524 computer results for, 519–520 experimental design and, 508–513 for factorial experiments, 539 for randomized block design, 532–533 ANOVA See Analysis of variance (ANOVA) ANOVA tables, 518–519, 589–590 Approximate class width, formula for, 65 Area as a measure of probability, 235–236 Assignable causes, 909 Association between two variables, measures of, 115–124 Attributes sampling plans, 930n3 Autocorrelation, 750 Average outgoing quality limit (AOQL), 930n2 Averages, 14–15 B Backward elimination, 741 Baldridge, Malcolm, 906 Baldridge Index, 906 Baldridge National Quality Program (BNQP), 906 Bar charts, 14f1.5, 34–36, 45n1 Barnett, Bob, 906 Bayes’ theorem, 157, 178–182, 183n1, 183n2 computing branch probabilities using, 960–965 tabular approach, 182 two-event case, 181 Bell curve See also Normal curve, 238–240 Bell Labs, 218 Bell Telephone Company, 905 Bernoulli, Jakob, 208 Bernoulli process, 208 Best-subsets regression, 741–742 Between-treatments estimate of population variance, 514–515 Between-treatments estimate of σ2, 511–512, 521n2 Bimodal data, 89 Binomial distribution for acceptance sampling, 930n1 expected value and variance for, 214–215 Binomial experiments, 208–209 Binomial probabilities normal approximation of, 250–252 tables, 213–214, 215n1, 215n2 Binomial probability distributions, 208 Binomial probability functions, 209, 212 Binomial sampling distribution, 861n2 Blocking, 530, 531 Bonferroni adjustment, 527–528 Bound on the sampling error, 22–7 Box plots, 110–111, 112n2 Burke Marketing Services, Inc., 507 Business Week, Butler Trucking Company, 646–648 C Case problems Air Force Training Program, 469 Alumni Giving, 705 alumni giving, 633 Bipartisan Agenda for Change, 501–502 business schools of Asia-Pacific, 139 compensation for sales professionals, 553–554 Consumer Research, Inc., 704–705 ethical behavior of business students, 397–398 forecasting food and beverage sales, 846–847 forecasting lost sales, 847–848 fuel economy for cars, 759–760 Gulf Real Estate Properties, 339–341 Hamilton County judges, 190–192 1072 Index Heavenly Chocolates website transactions, 139–141 lawsuit defense strategy, 969 measuring stock market risk, 631–632 Metropolitan Research, Inc., 341 motion picture industry, 72–73, 138–139 Par, Inc., 441–442 Pelican Stores, 71–72, 137–138 PGA tour statistics, 633–635, 705–707, 758–759 prediction winning percentage for the NFL, 708–709 Quality Associates, Inc., 396–397 Specialty Toys, Inc., 261–262 U.S Department of Transportation, 632–633 Wentworth Medical Center, 552–553 Young Professional magazine, 338–339 Categorical data, 7, 33–39 Categorical independent variables, 668–673 Categorical variables, Census, 15 Central limit theorem, 281–283, 286n2 Central location, measures of, 297n1 Chance events, 939 Chance nodes, 940 Chebyshev’s theorem, 104–105, 106–107n1 Chi-square distribution, 450–454 Chi-square test, 483n1 Cincinnatti Enquirer, 190 Citibank, 194 Classes, 39, 40 Class limits, 45n2 Class midpoints, 41, 127n1 Clusters, 298 Cluster sampling, 22–21–22–29, 298, 300n1 determining sample size, 22–26 population mean, 22–23–22–24 population total, 22–24–22–25 Coefficient of determination, 576–583, 579, 580n1, 692n2 Coefficient of variation, 99 Coefficients, interpretation of, 648–649 Colgate-Palmolive Company, 32 Combinations, 154 Common causes, 909 Company records, internal, 10 Comparisonwise Type I error rate, 527 Complements, 164, 165 Complete block design, 534 Completely randomized designs, 508, 513–524 Computers, 17 Conditional probabilities, 171–175, 960 Confidence coefficients, 313 Confidence intervals, 313, 594 for 1, 587–588 estimates, 323n2 for mean value of y, 595–596 Confidence levels, 313 Consequences, 939 Consistency, 297 Consumer Price Index (CPI), 764, 771 Consumer’s risk, 923 Contingency tables, 480 Continuity correction factor, 251 Continuous improvement, 909 Continuous random variables, 196 Control charts, 909–910 interpretation of, 920 np charts, 919–920 p charts, 917–919 R charts, 915–917 x chart, 910–915 Convenience sampling, 22–4, 299, 300n1 Cook’s distance measure, 679–681, 681n2 Correlation coefficient, 119–121, 579–580 Counting rules for combinations, 154 for multiple-step experiments, 151 for permutations, 154–155 Covariance, 115–119 Cravens, David W., 735 Critical value, 360 Critical value approach, 360–361 Crosby, Philip B., 905 Cross-sectional data, Cross-sectional regression, 786 Crosstabulations, 53–55 Cumulative frequency distributions, 43–44, 45n4 Cumulative percent frequency distributions, 44 Cumulative relative frequency distributions, 44 Customer’s Afternoon Letter, 772 Cyclical patterns, 789–791 D Data applications of, 580n1 bimodal and multimodal, 89 sources of, 10–13 types of, 5–8 Data acquistion errors, 13 Data errors, 681n1 Data mining, 17–18 Data sets, Data validity, 107n2 Data warehousing, 17 Decision analysis decision making with probabilities, 941–949 decision strategies, 951–954 decision trees, 940–941 payoff tables, 940 problem formulation, 939–941 with sample information, 949–960 Decision making, 381–382, 941–949 Decision nodes, 940 Decision strategies, 951–954 Decision trees, 940–941, 941n1, 941n2, 950–951 Deflating the series, 773–775 Degrees of belief, 156 Degrees of freedom, 316, 317, 319, 416, 535n1 DelGuzzi, Kristen, 190 Deming, W Edwards, 905 Dependent events, 175n1 Dependent variables, 562, 720–724 Descriptive statistics, 13–15, 127n1 Deseasonalized time series, 834–835, 837n2 Deviation about the mean, 97 1073 Index Discrete probability distributions, 197–200 Discrete probability functions, 198 Discrete random variables, 195 Discrete uniform probability distribution, 199 Discrete uniform probability function, 199 Distance intervals, 220 Distribution-free methods, 857 Distribution shape, 102–103 Doctrine of Chances, The (Moivre), 238–240 Dot plots, 41 Double-blind experiments, 513n2 Double-sample plans, 930 Dow, Charles Henry, 772 Dow Chemical Company, 904 Dow Jones averages, 772 Dow Jones Industrial Average (DJIA), 772 Duke Energy, Ch22–2 Dummy variables, 669 Duncan’s multipe range test, 528 Dunnhumby, 643 Durbin-Watson test, 751 E EAI problem, 283 Economics, Elements, 4–8, 5–6, 22–2 Empirical rule, 105–106 Error degrees of freedom, 535n1 Estimated logistic regression equation, 685–687 Estimated logit, 691 Estimated multiple regression equations, 644–645, 665–666 Estimated regression equations, 563–565, 567, 594, 612n2 Estimated standard deviation of b1, 586 Ethical behavior, 18–19 "Ethical Guidelines for Statistical Practice" (ASA), 18–19 Events, 160–162, 162n1, 164, 174 Excel analysis of variance with, 555–557 bar charts, 76–77 completely randomized design, 555 continuous probability distributions with, 262–263 crosstabulation, 79–81 descriptive statistics tool, 145–146 descriptive statistics using, 143–146 difference between two population means: σ1 and σ2 known, 444–445 difference between two population means: σ1 and σ2 unknown, 444–445 difference between two population means with matched samples, 445–446 discrete probability distributions with, 230–231 exponential smoothing, 851–852 factorial experiments, 556–557 forecasting with, 851–852 frequency distribution, 75–76, 77–79 goodness of fit test, 503, 504 histograms, 77–79 hypothesis testing with, 400–404 inferences about two populations, 444–446 interpretation of ANOVA output, 640 interpretation of estimated regression equation output, 639–640 interpretation of regression statistics output, 640 interval estimation using, 343–346 moving averages, 851 multiple regression with, 709–710 nonparametric methods with, 899–900 PivotChart, 77–79 PivotTable, 77–79 population mean: σ known, 343, 400–401 population mean: σ unknown, 344, 402–403 population proportion, 345–346, 403–404 population variances with, 470–471 Precision Tree add-in, 969–974 randomized block design, 555 random sampling with, 306–307 regression analysis, 638–640 scatter diagrams, 81–84 sign test, 899–900 Spearman rank correlation, 900 test of independence, 503, 505 trend projection, 852 using functions of, 143–145 Excel StatTools See StatTools, 17 Expected value, 202–203 binomial distribution and, 214–215 of p´, 289–290 of sample information, 954–956 of sample information (EVSI), 954–956 of x´, 279–280, 304 Expected value approach, 941–943 Expected value (EV), 942, 943–945 Experimental designs, 508–559 analysis of variance (ANOVA), 508–513 data collection, 509–510 multiple regression approach to, 745–749 Experimental studies, 11–12, 507 Experimental units, 508 Experiments, 150, 158n1 Experimentwise Type I error rate, 527 Exploratory data analysis, 48–51, 109–114, 112n1 Exponential distribution, 256n1, 258 Exponential probability density function, 258 Exponential probability distribution, 253–256 Exponential smoothing forecasting method, 800–804, 804n2 Exponential trend equation, 816 F Factorial experiments, 537–544 ANOVA procedure for, 539 F statistics for, 539–542 Factors, 508 Factors of interest, 531 F distribution, 460, 464n1, 516 Feigenbaum, A V., 905 Finance, 1074 Index Finite population correction factor, 280 Fisher, Ronald Alymer, Sir, 508 Fisher’s least significant difference (LSD), 524–527 Fitness for use, 905 Five-number summary, 109–110 Food Lion, 309 Forecast accuracy, 792–797, 799, 800, 802 mean absolute error (MAE), 793 mean absolute percentage error (MAPE), 793 mean squared error, 794 mean squared error (MSE), 793 Forecast errors, 792 Forecasting methods exponential smoothing, 800–804 moving averages, 797–800 seasonality and trend, 820–829 trend projection, 807–820 weighted moving averages, 800 Forecasts, 785 Forward selection, 740–741 Frames, 22–3, 267 Frequencies, 13t1.4 Frequency distributions, 33–34 classes, 39–41 number of classes in, 36n1 sum of, 36n2 F statistic, 732n1 F tests, 516, 588–590 for multiple regression models, 658–661 F test statistics, 461 F(x), 234 G Galton, Francis, Sir, 562 Gauss, Carl Friedrich, 567 General linear model, 714–729 curvilinear relationship modeling, 714–717 interaction, 718–720 nonlinear models that are intrinsically linear, 724–725 second-order model with one predictor variable, 715 simple first-order model with one predictor variable, 714 transformations involving the dependent variable, 720–724 Goodness of fit test, 474–477, 692n2 multinomial distribution, 476–477 normal distribution, 491–495 poisson distribution, 487–491 test statistic for, 475 Gossett, William Sealy, 316 Government agencies, 10–11 Grear Tire Company problem, 246–248 Grouped data, 125–127 population mean for, 127 population variance for, 127 sample mean for, 126 sample variance for, 126 G statistic, 692n1 H High leverage points, 617 Histograms, 14f1.6, 41–43, 45n1 Holt’s linear exponential smooting, 812–814, 817n1 Horizontal patterns, 786–788 Hypergeometric probability distribution, 221–223, 223n1 Hypergeometric probability function, 221–222 Hypothesis testing, 861n1 about a population median, 857–861 about μ1 Ϫ μ2, 410–412, 417–419 about p1 Ϫ p2, 431–433 confidence interval approach, 366 decision making and, 381–382 interval estimation and, 366–367 with matched samples, 862–863 null and alternative hypotheses, 349–353 one-tailed tests, 356–361, 371–372 population mean σ known, 356–370 population mean σ unknown, 370–376 population proportion, 376–381 for population variance, 454–457 steps of, 365 two-tailed tests, 362–365 Type I and Type II errors, 353–355 I Incomplete block design, 534 Independent events, 174, 175, 175n1 Independent sample design, 426n2 Independent simple random samples, 407 Independent variables, 508, 562, 662–663, 668–673, 743n2 Index numbers aggregate price indexes, 765–767 computing aggregate price index from price relatives, 769–779 deflating series by price indexes, 773–775 price indexes, 771–773 price relatives, 765 quantity indexes, 778–779 Index of Industrial Production, 779 Indicator variables, 669 Indifference quality level (IQL), 930n2 Influential observations, 616–618, 679, 681n1 using Cook’s distance measure to identify, 679–681 Interactions, 538–539, 718–720 International Organization for Standardization, 906 Interquartile range (IQR), 96–97 Intersection of two events, 166 Interval estimates, 309, 310–314, 594 of difference between two population means, 430 of population variance, 450–454 procedures for, 322–323 Interval estimation, 314n1, 409 difference between two population means: σ1 and σ2 known, 410 difference between two population means: σ1 and σ2 unknown, 416 Index hypothesis testing and, 366–367 of μ1 Ϫ μ2, 407–412, 415 of population mean: σ known, 313 of population proportion, 329, 330 Interval scales, Ishikawa, Karou, 905 ISO 9000, 906 Ith residual, 576 J John Morrell & Company, 349 Joint probabilities, 172, 962 Judgment sampling, 22–4, 299, 300n1 Juran, Joseph, 905 K K population means, 513n3 Kruskal-Wallis test, 882–884, 884n1 L Laspeyres index, 767 Least squares criterion, 567, 569n1, 645 Least squares estimated regression equation, 580n1 Least squares formulas, 635–636 Least squares method, 565–576, 569n1, 645–649 Levels of significance, 354 Leverage of observation i, 617, 676 LIFO (last-in, first-out) method of inventory valuation, 309 Linear exponential smoothing, 812–814 Linear trend regression forecast method, 807–812, 817n1 Logistic regression, 683–692, 692n2 Logit, 691 Logit transformation, 691 Lots, 922, 924 Lot tolerance percent defective (LPTD), 930n2 Lower control limits (LCL), 910 Lower tail tests, 356, 361 M Malcolm Baldridge National Quality Award, 906 Mann-Whitney-Wilcoxon (MWW) test, 871–882, 878n1 Marginal probabilities, 172 Margin of error, 309, 310–314, 323n1, 331n1 Marketing, Martin Clothing Store problem, 209–213 Matched samples, 423, 426n1, 426n2 Wilcoxon signed-rank test, 865–871 MeadWestvaco Corporation, 266 Mean, 14–15, 87–88, 124–125, 219 Mean absolute error (MAE), 793 Mean absolute percentage error (MAPE), 793 Mean square due to error (MSE), 521n3, 585, 793, 794 Mean square due to regression (MSR), 588 1075 Mean square due to treatments (MSTR), 514–515 Mean square regression (MSR), 588 Means square due to error (MSE), 515 Measures of association between two variables, 115–124 Measures of location, 87–92 Measures of variability, 95–102 Median, 88–89 Minitab, 17 Alpha to enter, 739–740 analysis of variance, 554–555 backward elimination procedure, 761 best-subsets procedure, 761 box plots, 143 completely randomized design, 554 continuous probability distributions with, 262–263 control charts, 935 covariance and correlation, 143 crosstabulation, 74–75 descriptive statistics using, 142–143 difference between two population means: σ1 and σ2 unknown, 442–443 difference between two population means with matched samples, 443 difference between two population proportions, 443–444 discrete probability distributions with, 230 dot plots, 73 exponential smoothing, 849 factorial experiments, 554–555 forecasting with, 848–851 forward selection procedure, 761 goodness of fit test, 502–503 histograms, 73–74 Holt’s linear exponential smoothing, 850 hypothesis testing with, 398–400 inferences about two populations, 442–444 interval estimation with, 341–343 Kruskal-Wallis test, 898–899 logistic regression with, 710 Mann-Whitney-Wilcoxon test, 898 moving averages, 848–849 multiple regression with, 708–709 nonparametric methods with, 896–899 population mean: σ known, 341–342, 398–399 population mean: σ unknown, 342, 399 population proportion, 342–343, 399–400 population variances with, 470 randomized block design, 554 random sampling with, 306 regression analysis with, 637–638 scatter diagrams, 74 sign test for a hypothesis test about a population mean, 896–897 sign test for a hypothesis test with matched samples, 897 Spearman rank correlation, 899 stem-and-leaf displays, 73–74 stepwise procedure, 760 test of independence, 503 time series decomposition, 850–851 trend projection, 849–850 1076 Index using for tabular and graphical presentations, 73–75 variable selection procedures, 760–761 Wilcoxon signed-rank test with matched samples, 897–899 Mode, 89 Model assumptions about the error term ε in the regression model, 583–584 confidence interval for 1, 587–588 F test, 588–590 for regression model, 584–585 t tests, 586 Model reliability, 18 Moivre, Abraham de, 238–240 Monsanto Company, 713 Motorola, Inc., 906 Moving averages forecasting method, 797–800, 804n2 MSE See Mean square due to error (MSE) MSE See Mean square due to error (MSE); Mean square error (MSE) MSR See Mean square due to regression (MSR); Mean square regression (MSR), 588 MSTR See Mean square due to treatments (MSTR) Multicollinearity, 662–663, 663n1 Multimodal data, 89 Multinomial distribution goodness of fit test, 476–477 Multinomial populations, 474 Multiple coefficient of determination, 654–655 Multiple comparison procedures Fisher’s least significant difference (LSD), 524–527 Type error rates, 527–528 Multiple regression analysis, 644, 692n2 Multiple regression equation, 644 Multiple regression model, 644, 657 Multiple sampling plans, 930 Multiplication law, 174–175 Multiplicative decomposition models, 830 Mutually exclusive events, 168, 175n1 N Nevada Occupational Health Clinic, 785 Nodes, 940 Nominal scales, Nonlinear trend regression, 814–816 Nonparametric methods, 857 Kruskal-Wallis test, 882–884 Mann-Whitney-Wilcoxon (MWW) test, 871–882 sign test, 857–865, 861n1 Spearman rank-correlation coefficient, 887–889 Wilcoxon signed-rank test, 865–871 Nonprobabilistic sampling, 22–4, 299, 300n1 Nonsampling errors, 22–5 Nonstationary time series, 804n2 Normal curve See also Bell curve, 238–240 Normal distribution goodness of fit test, 491–495 Normal probability density function, 239, 258 Normal probability distribution, 238–248 Normal probability plots, 610–612, 612n1 Normal scores, 610–612 Norris Electronics, 15–16, 19 Np chart, 910, 919–920, 920n2 Null hypothesis, 349–353 O Observational studies, 12–13, 507 testing for the equality of k population mean, 520–521 Observations, 6, 8n1 Oceanwide Seafood, 149 Odds in favor of an event occurring, 688 Odds ratio, 688–691, 692n1 Ogives, 44–45 Ohio Edison Company, 938 One-tailed tests, 371–372, 475 population mean σ known, 355–361 population mean σ unknown, 371–372 Open-end classes, 45n3 Operating characteristic (OC) curves, 925 Ordinal scales, Outliers, 106, 107n2, 614, 678, 681n1 Overall sample mean, 511 P Paasche index, 767 Parameters, 268 Parametric methods, 856 Partitioning, 518 Payoff, 940 Payoff tables, 940 P chart, 910 Pearson, Karl, 562 Pearson product moment correlation coefficient, 119–120, 889n1 Percent frequencies, 13t1.4 Percent frequency distributions, 34, 41 Percentiles, 90–91 Permutations, 154–155 Pie charts, 34–36 Planning values, 326 Point estimates, 274, 594 Point estimation, 273–275 Point estimators, 87, 274 consistency, 297 of difference between two population means, 409 of difference between two population proportions, 430 efficiency, 296–297 unbiased, 295–296 Poisson, Siméon, 218 Poisson probability distribution, 218–221 exponential distribution and, 255–256 goodness of fit test, 487–491 Poisson probability function, 218, 488 Pooled estimate of σ2, 512 Pooled estimator of p, 432 Pooled sample variance, 419n1 Population mean, 22–6–22–7, 22–12–22–14 1077 Index approximate 95% confidence interval estimate of, 22–13, 22–25 for grouped data, 127 inference about difference between: matched samples, 423–426 inference about difference between: σ1 and σ2 known, 407–412 inference about difference between: σ1 and σ2 unknown, 415 point estimator of, 22–12, 22–23–22–24 sample size when estimating, 22–17 σ known, 310–314 σ unknown, 316–323 Population mean σ known interval estimates, 310–314 margin of error, 310–314 one-tailed tests, 355–361 Population mean σ unknown hypothesis testing, 370–376 interval estimate, 317–320 margin of error, 317–320 two tailed testing, 372–373 Population parameters, 87 Population proportions, 22–8–22–9, 22–15–22–16, 328–331, 331n1 approximate 95% confidence interval estimate of, 22–15, 22–26 hypothesis testing and, 376–381 inferences about difference between, 429–436 interval estimate of, 329 interval estimation of p1 Ϫ p2, 429–431 normal approximation of sampling distribution of, 328 point estimator of, 22–15, 22–25 sample size for an interval estimate of, 330 test statistic for hypothesis tests about, 378 Populations, 15, 22–2 Population standard deviation (σ), 99, 310 Population total, 22–7–22–8, 22–14–22–15 approximate 95% confidence interval estimate of, 22–14, 22–25 point estimator of, 22–14, 22–24 sample size when estimating, 22–18 Population variance, 97 between-treatments estimate of, 514–515 for grouped data, 127 hypothesis testing for, 454–457 inferences about, 450–459 within-treatments estimate of, 515–516 Posterior (revised) probabilities, 178, 949 Power, 385 Power curves, 385 Precision Tree add-in to Excel, 969–974 Prediction intervals, 594 Prediction intervals for individual value of y, 596–598 Price indexes Consumer Price Index (CPI), 771 deflating a series by, 773–775 Dow Jones averages, 772 Producer Price Index (PPI), 771 quality changes, 777–778 selection of base period, 777 selection of items, 777 Price relatives, 765, 769–770 Prior probabilities, 178, 949 Probabilistic sampling, 22–4, 300n1 Probabilities, 150 classical method of assigning, 155–156, 162n1 conditional, 171–175 joint, 172 marginal, 172 posterior, 178 prior, 178 relative frequency method of assigning, 156 subjective method for assigning, 156–155 of success, 215n1, 215n2 Probability density function, 234, 237n1 Probability distributions, 197 Probability functions, 197 Probability samples, 271n2, 513n1 Procter & Gamble, 233 Producer Price Index (PPI), 771 Producer’s risk, 923 Production, Proportional allocation, 22–19n2 P-value approach, 358–360 P-values, 358, 367n1 Q Quadratic trend equation, 814–816 Quality assurance, 908 Quality control, 905–908 Quality engineering, 908 Quantitative data, 7, 8n2, 33 class limits and, 45n2 summarizing, 39–53 Quantitative variables, Quantity indexes, 778–779 Quartiles, 91–92 Questionnaires, 22–3 R Random experiments, 158n1 Randomization, 508, 513n1 Randomized block design, 530–537, 535n1 Random samples, 158n2, 270, 271n1 Random variables, 194–196, 196n1 Range, 96 Ratio scales, R charts, 910, 915–917, 920n1 Regression analysis, 562, 565n1, 618n1 adding or deleting variables, 729–735 autocorrelation and the Durbin-Watson test, 750–754 computer solutions, 600–601 general linear model, 714–725 of larger problems, 735–738 multiple regression approach to experimental design, 745–749 residuals, 793 variable selection procedures, 739–745 1078 Index Regression equations, 563–565, 565n2 Regression models, 562, 743n3 Regression sums of squares, 732n1 Rejectable quality level (RQL), 930n2 Rejection rule for lower tail test critical value approach, 361 Rejection rules using p-value, 360 Relative efficiency, 295–296 Relative frequency distributions, 34, 41 formula for, 65 Replication, 509 Replications, 538 “Researches on the Probability of Criminal and Civil Verdicts” (Poisson), 218 Residual analysis, 605–614, 612n2 detecting influential observations, 616–618 detecting outliers, 614–616, 678 influential observations, 679 of multiple regression model, 676–681 normal probability plots, 610–612 residual for observation i, 605 residual plot against x, 606–607 residual plot against yˆ, 607 standard deviation of residual i, 676 standardized residuals, 607–610 standard residual for observation i, 676 Residual plots, 606, 612n1 against x, 606–607 against yˆ, 607 Residuals, 793 Response variables, 508 Reynolds, Inc., 714–717 Rounding errors, 100n3 S Sample correlation coefficients, 119–120, 579–580 Sampled populations, 22–3, 267 Sample information, 949 expected value of (EVSI), 954–956 Sample mean, 126, 267, 297n1, 521n1 Sample points, 150 Samples, 15, 22–2, 271n1 Sample selection, 268–271 from infinite population, 270–271 random samples, 270 sampling withouth replacement, 269 sampling with replacement, 270 Sample size determining, 325–327 estimating population mean, 22–17 estimating population total, 22–18 for hypothesis test about a population mean, 387–390 for interval estimate of population mean, 326 outliers and, 320 of population proportion, 330 sampling distribution of x´ , 285–286 skewness and, 320 small samples, 320–322 Sample space, 150 Sample statistics, 87, 273–274 Sample surveys, 15, 22–2–22–3 Sample variance, 97, 100n4, 126 Sampling distributions, 276–286 of b1, 586 of (n-1)s2/σ2, 450 of p´, 289–293 of two population variances, 460 of x´, 278–279, 281–286 Sampling units, 22–3 Sampling without replacement, 269 Sampling with replacement, 270 Scales of measurement, 6–7 Scatter diagrams, 57–59, 565 Seasonal adjustments, 836 Seasonal indexes, calculating, 830–834, 837n1 Seasonality and trend, 820–829 models based on monthly data, 825–826 seasonalty without trend, 820–823 Seasonal patterns, 788–789 Second-order model with one predictor variable, 715 Serial correlation, 750 Shewhart, Walter A., 905 Significance testing, 585–594, 590–591 using correlation, 636–637 Sign tests, 857–861, 861n2 hypothesis test about a population median, 857–861 hypothesis test with matched sample, 862–863 Simple first-order model with one predictor variable, 715 Simple linear regression, 562, 565n2 F test for significance in, 589 Simple random samples, 22–6–22–12, 271–272n2, 271n2, 300n1 determining sample size, 22–9–22–11 finite populations, 268–270 population mean, 22–6–22–7 population proportion, 22–8–22–9 population total, 22–7–22–8 Simple regression, 692n2 Simpson’s paradox, 56–57 Single-factor experiments, 508 Single-sample plans, 930 Single-stage cluster sampling, 22–21 Six Sigma, 906–908 limits and defects per million opportunities (dpmo), 907 Skewed distributions, 256n1 Skewed populations, 323n2 Skewness, 102–103, 256n1, 323n2 ⌺ known, 310 Small Fry Design, 86 Smoothing constants, 800, 801 Software packages, 17, 18 Spearman rank-correlation coefficient, 887–889, 889n1 Spreadsheet packages, 804n1 SSE See Sum of squares due to error (SSE) SSR See Sum of squares due to regression (SSR) SST See Total sum of squares SSTR See Sum of squares due to treatments (SSTR), 515 1079 Index Standard deviation, 99, 204 of the ith residual, 609 of p´, 290 of x´, 280–281, 304–305 Standard error, 281 of p1 Ϫ p2, 430 of p1 Ϫ p2 when p1 ϭ p2 ϭ p, 432 two independent random samples, 409 Standard error of the estimate, 585 Standard error of the proportion, 290 Standardized residual for observation i, 610 Standardized residuals, 607–610 Standard normal probability distribution, 240–245, 245–248 Standard normal random variable, 245, 258 States of nature, 939 Stationary assumption, 209 Stationary time series, 787, 804n2 Statistical analysis, 17 Statistical experiments, 158n1 Statistical inference, 15–16 Statistical models, 18 Statistical process control, 908–922 assignable causes, 909 common causes, 909 np chart, 919–920 p chart, 917–919 R chart, 915–917 x´ chart, 909–915 Statistical significance vs practical significance, 591n2 Statistical software packages, 100n1, 272n3 Statistical studies, 11–13 Statistics, StatTools analysis of completely randomized design, 556–559 box plots, 147 control charts, 935–936 covariance and correlation, 147 descriptive statistics using, 146–147 exponential smoothing, 853 forecasting with, 852–854 getting started with, 28–30 histograms, 84 Holt’s linear exponential smoothing, 853–854 hypothesis testing with, 404–405 hypothesis tests about μ1 Ϫ μ2, 446–447 inferences about the difference betweentwo populations: matched samples, 447 inferences about two populations, 446–447 interval estimation of μ1 Ϫ μ2, 446 interval estimation of population mean: σ unknown case, 346 interval estimation with, 346–347 Mann-Whitney-Wilcoxon test, 901–902 moving averages, 852–853 multiple regression analysis with, 711 nonparametric methods with, 901–902 population mean: σ unknown case, 404–405 random sampling with, 307 regression analysis, 640–641 sample size, determining, 346–347 scatter diagrams, 84 single population standard deviation with, 471 using for tabular and graphical presentations, 75–84 variable selection procedures, 761–762 Wilcoxon signed-rank test with matched samples, 901 Stem-and-leaf display, 48–51 Stepwise regression procedure, 739–740, 743n1 Stocks and stock funds, 100n2 Stratified random sampling, 297–298, 300n1 Stratified simple random sampling, 22–19n1 advantages of, 22–19n1 population mean, 22–12–22–14 population proportion, 22–15–22–16 population total, 22–14–22–15 Studentized deleted residuals, 678–679 Sum of squares due to error (SSE), 515–516, 576 Sum of squares due to regression (SSR), 557 Sum of squares due to treatments (SSTR), 515 Sum of the squares of the deviations, 566 ⌺ unknown, 316 Survey errors, 22–5–22–6 Surveys and sampling methods, 22–3–22–4 Systematic sampling, 22–29, 298–299, 300n1 T T, 586, 658–661 Taguchi, Genichi, 905 Target populations, 22–3, 275 T distribution, 316, 317 Test for significance, 585–594, 591n1, 591n3, 636–637, 658–663, 687 Test for the equality of k population means, 517, 520–521 Test of independence, 479–483 Test statistics, 357–358 for chi-square test, 483n1 for the equality of a k population means, 516 for goodness of fit, 475 for hypothesis tests about a population variance, 454 hypothesis tests about μ1 Ϫ μ2: σ1 and σ2 known, 411 for hypothesis tests about population mean: σ known, 358 for hypothesis tests about p1 Ϫ p2, 432 for hypothesis tests about two population variances, 461 for hypothesis tests involving matched samples, 425 hypothesis tests μ1 Ϫ μ2: σ1 and σ2 unknown, 417–419 Thearling, Kurt, 17 Time intervals Poisson probability distribution and, 218–220 Time series, 786–792 Time series data, deflating by price indexes, 773–775 graphs of, 9f1.2 Time series decomposition, 829–839 additive decompostion model, 829–830 1080 Index calculating seasonal indexes, 830–834 cyclical components, 837 deseasonalized time series, 834 models based on monthly data, 837 multiplicative model, 830 seasonal adjustments, 836 Time series patterns, 786–792 cyclical, 789–791 horizontal pattern, 786–788 seasonal patterns, 788–789 selecting forecasting methods, 791–792 trend and seasonal pattern, 788 trend pattern, 788 Time series plots, 786–792 Time series regression, 786 Total quality (TQ), 904 Total sum of squares (SST), 577 Treatments, 508 Tree diagrams, 152 Trend and seasonal patterns, 789 Trendlines, 57–59 Trend patterns, 788 Trend projection Holt’s linear exponential smoothing, 812–814 linear trend regression, 807–812 nonlinear trend regression, 814–817 Trimmed mean, 92n1 T tests, 586 for individual significane in multiple regression models, 661–662 for significance in simple linear regression, 587 Tukey's procedure, 528 Two population variances inferences about, 460–465 one-tailed hypothesis test about, 461 sampling distribution of, 460 Two-stage sampling plans, 930 Two-tailed tests, 362–367 computation of p-value, 364 critical value approach, 364 population mean σ known, 362–365 population mean σ unknown, 372–373 p-value approach, 363 Type I errors, 353–355, 355n1 comparisonwise Type I error rate, 527 experimentwise Type I error rate, 527 Type II errors, 353–355, 355n1 probability of, 382–385 U Unbiased estimators, 295–296 Uniform probability density function, 234, 258 Uniform probability distribution, 234–237 Union of two events, 165 United Way, 473 Upper control limits (UCL), 910 Upper tail tests, 356, 361, 461 U.S Commerce Department National Institute of Standards and Technology (NIST), 906 U.S Department of Labor Bureau of Labor Statistics, 764 U.S Food and Drug Administration, 407 U.S Government Accountability Office, 449 V Variability, measures of, 95–102 Variables, 5–6 adding or deleting, 729–735 random, 194–196 use of p-values, 732 Variable selection procedures Alpha to enter, 739–740 backward elimination, 741 best-subsets regression, 741–742 forward selection, 740–741 stepwise regression, 739–740 Variables sampling plans, 930n3 Variance, 97–99, 203–204 binomial distribution and, 214–215 Poisson probability distribution and, 219 Venn diagrams, 164 W Weighted aggregate price indexes, 766 Weighted means, 124–125 Weighted moving averages forecasting method, 800 Western Electric Company, 905 West Shell Realtors, 856 Wilcoxon signed-rank test, 865–871, 868n1, 868n2 Williams, Walter, 355, 355n1 Within-treatments estimate of population variance, 515–516 Within-treatments estimate of σ2, 512 X X chart x´, 909, 920n1 process mean and standard deviation known, 910–912 process mean and standard deviation unknown, 912–915 Z Z-scores, 103–104, 106 Z test, 692n1 Statistics for Business and Economics 11eWEBfiles Chapter Morningstar Norris Shadow02 Table 1.1 Table 1.5 Exercise 25 Chapter EAI MetAreas MutualFund Section 7.1 Appendix 7.2, 7.3 & 7.4 Exercise 14 Chapter ApTest Audit BestTV Broker CityTemp Computer Crosstab DYield DJIAPrices FedBank Fortune Frequency FuelData08 GMSales Holiday LivingArea Major Marathon Movies MutualFunds Names Networks NewSAT OffCourse PelicanStores Population Restaurant Scatter SoftDrink Stereo SuperBowl Table 2.8 Table 2.4 Exercise Exercise 26 Exercise 46 Exercise 21 Exercise 29 Exercise 41 Exercise 17 Exercise 10 Exercise 51 Exercise 11 Exercise 37 Exercise 40 Exercise 18 Exercise Exercise 39 Exercise 28 Case Problem Exercise 34 Exercise Exercise Exercise 42 Exercise 20 Case Problem Exercise 44 Table 2.9 Exercise 30 Table 2.1 Table 2.12 Exercise 43 Chapter 3Points Ages Asian BackToSchool CellService Disney Economy FairValue Homes Hotels Housing MajorSalary MLBSalaries Movies Mutual NCAA PelicanStores Penalty PropertyLevel Runners Shoppers Speakers SpringTraining StartSalary Stereo StockMarket TaxCost Travel Visa WorldTemp Exercise Exercise 59 Case Problem Exercise 22 Exercise 42 Exercise 12 Exercise 10 Exercise 67 Exercise 64 Exercise Exercise 49 Figure 3.7 Exercise 43 Case Problem Exercise 44 Exercise 34 Case Problem Exercise 62 Exercise 65 Exercise 40 Case Problem Exercise 35 Exercise 68 Table 3.1 Table 3.6 Exercise 50 Exercise Exercise 66 Exercise 58 Exercise 51 Chapter Judge Case Problem Chapter Volume Exercise 24 Chapter 12 Chemline FitTest Independence NYReform Table 12.10 Appendix 12.2 Appendix 12.2 Case Problem Chapter ActTemps Alcohol Auto Flights GulfProp Interval p JobSatisfaction JobSearch Lloyd’s Miami NewBalance Nielsen NYSEStocks Professional Program Scheer TaxReturn TeeTimes TicketSales Exercise 49 Exercise 21 Case Problem Exercise 48 Case Problem Appendix 8.2 Exercise 37 Exercise 18 Section 8.1 Exercise 17 Table 8.3 Exercise Exercise 47 Case Problem Exercise 20 Table 8.4 Exercise Section 8.4 Exercise 22 Chapter AgeGroup AirRating Bayview Coffee Diamonds Drowsy Eagle FirstBirth Fowle Gasoline GolfTest Hyp Sigma Known Hyp Sigma Unknown Hypothesis p Orders Quality UsedCars WomenGolf Exercise 39 Section 9.4 Case Problem Section 9.3 Exercise 29 Exercise 44 Exercise 43 Exercise 64 Exercise 21 Exercise 67 Section 9.3 Appendix 9.2 Appendix 9.2 Appendix 9.2 Section 9.4 Case Problem Exercise 32 Section 9.5 Chapter 10 AirFare Cargo CheckAcct Earnings2005 ExamScores Golf GolfScores HomePrices Hotel Matched Mutual Occupancy PriceChange SAT SATVerbal SoftwareTest TaxPrep TVRadio Exercise 24 Exercise 13 Section 10.2 Exercise 22 Section 10.1 Case Problem Exercise 26 Exercise 39 Exercise Table 10.2 Exercise 40 Exercise 46 Exercise 42 Exercise 18 Exercise 16 Table 10.1 Section 10.4 Exercise 25 Chapter 13 AirTraffic Assembly AudJudg Browsing Chemitech Exer6 Funds GMATStudy GrandStrand HybridTest MarketBasket Medical1 Medical2 NCP Paint RentalVacancy SalesSalary SatisJob SATScores SnowShoveling Triple-A Vitamins Chapter 14 Absent AgeCost Alumni Armand’s Beer Beta Boots Ellipticals ExecSalary HomePrices HondaAccord HoursPts Hydration1 Hydration2 IPO IRSAudit Jensen JetSki JobSat Laptop MktBeta NFLValues OnlineEdu PGATour PlasmaTV RaceHelmets Safety Sales SleepingBags SportyCars Stocks500 Suitcases Chapter 11 Bags BusTimes PriceChange Return SchoolBus Training Travel Yields Exercise 19 Section 11.1 Exercise Exercise Section 11.2 Case Problem Exercise 25 Exercise 11 Table 13.5 Exercise 38 Exercise 10 Exercise 39 Table 13.1 Exercise Exercise 36 Table 13.10 Exercise 12 Exercise 32 Exercise 41 Case Problem Case Problem Table 13.4 Exercise 11 Exercise 37 Case Problem Exercise 35 Exercise 26 Exercise 27 Exercise 20 Exercise 25 Exercise 63 Exercise 64 Case Problem Table 14.1 Exercise 52 Case Problem Exercise 27 Exercises 5, 22, & 30 Exercise 10 Exercise 49 Exercise Exercise 65 Exercise 43 Exercise 53 Exercise 58 Exercise 67 Exercise 61 Exercise 12 Exercise 68 Exercise 14 Exercise 66 Exercise 54 Exercise 60 Case Problem Exercise 20 Exercise 44 Case Problem Exercises & 19 Exercises 8, 28, & 36 Exercise 11 Exercise 59 Exercise Chapter 15 Alumni Auto2 Bank Basketball Boats Brokers Case Problem Exercise 42 Exercise 46 Exercise 24 Exercises 9, 17, & 30 Exercise 25 Butler Tables 15.1 & 15.2 Chocolate Exercise 48 Consumer Case Problem Exer2 Exercise FuelData Exercise 57 Johnson Table 15.6 Lakeland Exercise 47 Laptop Exercise LPGA Exercise 43 MLB Exercises &16 MutualFunds Exercise 56 NBA Exercises 10, 18, & 26 NFLStats Case Problem PGATour Case Problem Repair Exercise 35 RestaurantRatings Exercise 37 Sedans Exercise Showtime Exercises 5, 15, & 41 Simmons Table 15.11 & Exercise 44 SportsCar Exercise 31 Stroke Exercise 38 TireRack Exercise 54 Treadmills Exercise 55 Chapter 16 Audit Bikes Browsing Cars Chemitech ClassicCars ColorPrinter Cravens IBM Layoffs LightRail LPGATour LPGATour2 MetroAreas MLBPitching MPG PGATour Resale Reynolds Stroke Tyler Yankees Exercise 31 Exercise 30 Exercise 34 Case Problem Table 16.10 Exercise Exercise 29 Table 16.5 Exercise 27 Exercise 16 Exercise Exercises 12 & 13 Exercise 17 Exercise Exercise 15 Table 16.4 Case Problem Exercise 35 Table 16.1 Exercises 14 & 19 Table 16.2 Exercise 18 Chapter 18 AptExp Bicycle CarlsonSales CDSales Cholesterol CountySales ExchangeRate Gasoline GasolineRevised HudsonMarine Masters NFLValue Pasta PianoSales Pollution Exercises 34 & 38 Tables 18.3 & 18.12 Case Problem Exercise 45 Tables 18.4 & 18.16 Case Problem Exercise 24 Table 18.1 & Exercises 7, 8, & Table 18.2 Exercise 53 Exercise 16 Exercise 27 Exercise 26 Exercise 49 Exercises 31 & 39 Power SouthShore TextSales TVSales Umbrella Vintage Exercises 33 & 40 Exercise 32 Exercise 37 Tables 18.6 & 18.19 Tables 18.5 & 18.17 Case Problem Chapter 19 AcctPlanners Additive ChicagoIncome CruiseShips Evaluations Exercise 19 Exercise 12 Exercise Exercise 29 Exercise 45 Exams GolfScores HomeSales Hurricanes JapanUS MatchedSample Methods Microware NielsenResearch OnTime Overnight PoliceRecords PotentialActual Exercise 46 Exercise 16 Section 19.1 Exercise 21 Exercise 22 Appendix 19.1 & 19.3 Exercise 43 Exercise 24 Exercise 47 Exercise 14 Exercise 15 Exercise 23 Table 19.16 ProductWeights Professors ProGolfers Programs Refrigerators Relaxant Student SunCoast Techs TestPrepare ThirdNational Williams WritingScore Exercise 42 Exercise 37 Exercise 36 Exercise 44 Exercise 40 Exercise 13 Exercise 34 Appendix 19.1 Exercise 35 Exercise 27 Appendix 19.1 & 19.3 Appendix 19.1 Exercise 17 Chapter 20 Coffee Jensen Tires Exercise 20 Table 20.2 Exercise Chapter 21 PDC Tree Appendix 21.1 Appendix F p-Value Appendix F ... 9988 9988 9989 9989 9989 9990 9990 STATISTICS FOR BUSINESS AND ECONOMICS 11e This page intentionally left blank STATISTICS FOR BUSINESS AND ECONOMICS 11e David R Anderson University of Cincinnati... 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. .. 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