Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Interval Nominal Ordinal DATA TYPES Wilcoxon signed rank sum test Section 19-2 Box plot Section 4-3 Sign test Section 19-2 Percentiles and quartiles Section 4-3 Box plot Section 4-3 Wilcoxon rank sum test Section 19-1 Chi-squared test of a contingency table Section 15-2 Median Section 4-1 Chi-squared goodness-offit test Section 15-1 z -test and estimator of a proportion Section 12-3 Pie chart Section 2-2 Bar chart Section 2-2 Frequency distribution Section 2-2 Chi-squared test and estimator of a variance Section 12-2 z -test and estimator of the difference between two proportions Section 13-5 Wilcoxon rank sum test Section 19-1 Percentiles and quartiles Section 4-3 t -test and estimator of a mean Section 12-1 F -test and estimator of ratio of two variances Section 13-4 t -test and estimator of mean difference Section 13-3 Unequal-variances t -test and estimator of the difference between two means: independent samples Section 13-1 Equal-variances t -test and estimator of the difference between two means: independent samples Section 13-1 Compare Two Populations Range, variance, and standard deviation Section 4-2 Mean, median, and mode Section 4-1 Line chart Section 3-2 Stem-and-leaf Section 3-1 Ogive Section 3-1 Histogram Section 3-1 Describe a Population Pr o blem Objectives Friedman test Section 19-3 Kruskal-Wallis test Section 19-3 Chi-squared test of a contingency table Section 15-2 Friedman test Section 19-3 Kruskal-Wallis test Section 19-3 Two-factor analysis of variance Section 14-5 Two-way analysis of variance Section 14-4 Spearman rank correlation Section 19-4 Chi-squared test of a contingency table Section 15-2 Spearman rank correlation Section 19-4 Simple linear regression and correlation Chapter 16 Least squares line Section 4-4 Coefficient of determination Section 4-4 Coefficient of correlation Section 4-4 Covariance Section 4-4 LSD multiple comparison method Section 14-2 Tukey’s multiple comparison method Section 14-2 Scatter diagram Section 3-3 Analyze Relationship between Two Variables One-way analysis of variance Section 14-1 Compare Two or More Populations A GUIDE TO STATISTICAL TECHNIQUES Not covered Not covered Multiple regression Chapters 17 & 18 Analyze Relationship among Two or More Variables AMERICAN NATIONAL ELECTION SURVEY AND GENERAL SOCIAL SURVEY EXERCISES Chapter ANES Page 12 13 14 15 16 17 18 19 3.62–3.67 4.37–4.38 4.58–4.60 4.93 12.63–12.65 12.133–12.135 13.86–13.88 13.116–13.117 13.181–13.183 81 114 123 140 400 423 464 480 505 A13.27–A13.30 14.37–14.42 13.65–13.68 14.86–14.87 A14.24–A14.25 15.17 15.55–15.58 A15.25–A15.27 16.45–16.49 16.85–16.88 A16.27–A16.28 17.21–17.22 A17.27–A17.28 18.33 18.42 19.31–19.34 19.58–19.59 19.92–19.93 19.116–19.117 A19.44–A19.51 518 537 548 559 590 599 611 626 659 666 684 706 725 746 755 775 790 802 811 828 GSS Page 2.34–2.39 3.23–3.28 3.68–3.71 4.39 4.61–4.62 4.91–4.92 12.46–12.62 12.117–12.132 13.42–13.85 13.114–13.115 13.163–13.180 13.225–13.231 A13.18–A13.126 14.23–14.36 14.53–14.64 14.84–14.85 A14.19–A14.23 15.18–15.21 15.47–15.54 A15.17–A15.24 16.50–16.65 16.89–16.100 A16.17–A16.26 17.16–17.20 A17.17–A17.26 18.24–18.32 18.38–18.41 19.17–19.30 32 63 81 114 123 140 400 422 462 480 504 512 518 537 547 559 590 599 610 626 660 666 684 705 725 745 755 775 19.85–19.91 19.107–19.115 A19.26–A19.43 802 810 827 APPLICATION SECTIONS Section 4.5 (Optional) Application in Professional Sports Management: Determinants of the Number of Wins in a Baseball Season (illustrating an application of the least squares method and correlation) 140 Section 4.6 (Optional) Application in Finance: Market Model (illustrating using a least squares lines and coefficient of determination to estimate a stock’s market-related risk and its firm-specific risk) 144 Section 7.3 (Optional) Application in Finance: Portfolio Diversification and Asset Allocation (illustrating the laws of expected value and variance and covariance) 233 Section 12.4 (Optional) Application in Marketing: Market Segmentation (using inference about a proportion to estimate the size of a market segment) 423 Section 14.6 (Optional) Application in Operations Management: Finding and Reducing Variation (using analysis of variance to actively experiment to find sources of variation) 573 Section 18.3 (Optional) Human Resources Management: Pay Equity (using multiple regression to determine cases of discrimination) 746 APPLICATION SUBSECTION Section 6.4 (Optional) Application in Medicine and Medical Insurance: Medical Screening (using Bayes’s Law to calculate probabilities after a screening test) 200 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Statistics for Management and Economics 10e (&3"-%,& &3 Professor Emeritus, Wilfrid Laurier University "VTUSBMJBr#SB[JMr+BQBOr,PSFBr.FYJDPr4JOHBQPSFr4QBJOr6OJUFE,JOHEPNr6OJUFE4UBUFT Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it This is an electronic version of the print textbook Due to electronic rights restrictions, some third party content may be suppressed Editorial review has deemed that any suppressed content does not materially affect the overall learning experience The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Statistics for Management and Economics, Tenth Edition Gerald Keller Senior Vice President, Global Product Manager, Higher Education: Jack W. Calhoun Product Director: Joe Sabatino Product Manager: Aaron Arnsparger Content Developer: Kendra Brown © 2014, 2009 Cengage Learning WCN: 02-300 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 Product Assistant: Brad Sullender Marketing Manager: Heather Mooney Sr Content Project Manager: Holly Henjum Media Developer: Chris Valentine Manufacturing Planner: Ron Montgomery Production House/Compositor: diacriTech 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 www.cengage.com/permissions Further permissions questions can be emailed to permissionrequest@cengage.com Sr Rights Acquisitions Specialist: John Hill Sr Art Director: Stacy Jenkins Shirley Internal Designer: KeDesign/cmiller design Cover Designer: cmiller design Cover Images: © Quan Long/Getty Images ExamView® and ExamView Pro® are registered trademarks of FSCreations, Inc 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: 2013946385 Student Edition ISBN 13: 978-1-285-42545-0 Student Edition ISBN 10: 1-285-42545-6 Cengage Learning 200 First Stamford Place, 4th Floor Stamford, CT 06902 USA Cengage Learning products are represented in Canada by Nelson Education, Ltd To learn more about Cengage Learning Solutions, visit www.cengage.com Purchase any of our products at your local college store or at our preferred online store www.cengagebrain.com Printed in Canada 17 16 15 14 13 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Brief Contents What Is Statistics? Graphical Descriptive Techniques I 11 Graphical Descriptive Techniques II 43 Numerical Descriptive Techniques 94 Data Collection and Sampling 158 Probability 172 Random Variables and Discrete Probability Distributions 213 Continuous Probability Distributions 259 Sampling Distributions 301 10 Introduction to Estimation 324 11 Introduction to Hypothesis Testing 347 12 Inference about a Population 385 13 Inference about Comparing Two Populations 437 14 Analysis of Variance 519 15 Chi-Squared Tests 591 16 Simple Linear Regression and Correlation 628 17 Multiple Regression 686 18 Model Building 727 19 Nonparametric Statistics 759 20 Time-Series Analysis and Forecasting 829 iii Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it iv BRIEF CONTENTS 21 Statistical Process Control 859 22 Decision Analysis 887 23 Conclusion 907 Appendix A Data File Sample Statistics A-1 Appendix B Tables B-1 Appendix C Answers to Selected Even-Numbered Exercises C-1 Index I-1 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Contents What Is Statistics? 1-1 1-2 1-3 1-4 Appendix Graphical Descriptive Techniques I 11 2-1 2-2 2-3 Introduction 12 Types of Data and Information 13 Describing a Set of Nominal Data 18 Describing the Relationship between Two Nominal Variables and Comparing Two or More Nominal Data Sets 32 Graphical Descriptive Techniques II 43 3-1 3-2 3-3 3-4 Introduction Key Statistical Concepts Statistical Applications in Business Large Real Data Sets Statistics and the Computer Instructions for Keller’s Website 10 Introduction 44 Graphical Techniques to Describe a Set of Interval Data 44 Describing Time-Series Data 64 Describing the Relationship between Two Interval Variables 73 Art and Science of Graphical Presentations 81 Numerical Descriptive Techniques 94 4-1 4-2 4-3 4-4 4-5 4-6 4-7 Introduction 95 Sample Statistic or Population Parameter 95 Measures of Central Location 95 Measures of Variability 105 Measures of Relative Standing and Box Plots 114 Measures of Linear Relationship 123 (Optional) Applications in Professional Sports: Baseball 140 (Optional) Applications in Finance: Market Model 144 Comparing Graphical and Numerical Techniques 148 v Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it vi CONTENTS 4-8 Appendix Data Collection and Sampling 158 5-1 5-2 5-3 5-4 7-4 7-5 Introduction 214 Random Variables and Probability Distributions 214 Bivariate Distributions 225 (Optional) Applications in Finance: Portfolio Diversification and Asset Allocation 233 Binomial Distribution 240 Poisson Distribution 248 Continuous Probability Distributions 259 8-1 8-2 8-3 8-4 Introduction 173 Assigning Probability to Events 173 Joint, Marginal, and Conditional Probability 177 Probability Rules and Trees 188 Bayes’s Law 196 Identifying the Correct Method 206 Random Variables and Discrete Probability Distributions 213 7-1 7-2 7-3 Introduction 159 Methods of Collecting Data 159 Sampling 162 Sampling Plans 164 Sampling and Nonsampling Errors 169 Probability 172 6-1 6-2 6-3 6-4 6-5 General Guidelines for Exploring Data 151 Review of Descriptive Techniques 156 Introduction 260 Probability Density Functions 260 Normal Distribution 266 (Optional) Exponential Distribution 283 Other Continuous Distributions 287 Sampling Distributions 301 9-1 9-2 Introduction 302 Sampling Distribution of the Mean 302 Sampling Distribution of a Proportion 313 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net I-5 INDEX Graphical descriptive techniques, 12–13 bar and pie charts, 21–25 control charts, 862–867 deception in, 83–87 decision trees, 889–890 excellence in, 81–82 histograms, 46–57 for interval data, 44–61 line charts, 64–66 numerical descriptive techniques compared with, 148–149 ogives, 59–61 p charts, 882 probability trees, 192–194 for relationship between two nominal variables, 35–36, 600 scatter diagrams, 73–78 S charts, 875–878 stem-and-leaf displays, 57–59 summary of, 156–157 for time-series data, 64–68 x charts, 868–875 Graphical excellence, 81–82 Grouped data, approximating mean and variance for, 112 Heteroscedasticity, 669 Histograms, 44, 46–57 Chebysheff’s Theorem for, 111 Holmes, Oliver Wendell, 336 Homoscedasticity, 669 Human resources management applications pay equity, 746–751 performance measurement for, 744–745 retention of workers, 640 severance pay, 701 testing job applicants, 641 Hypothesis testing, 335–338 calculating probability of Type II errors in, 359–366 determining alternative hypothesis for null hypothesis, 365–366 in statistical process control, 862–864 testing population means with known standard deviation, 339–355 Independence, of events, 182 multiplication rule for, 189 Independent samples, 549 Wilcoxon rank sum test for, 762–772 Independent variables, 629 multicollinearity among, 707–708 in multiple regression analysis, 686, 689 nominal, 737–742 in polynomial models, 728–729 stepwise regression for, 752–755 Indicator variables (dummy variables), for nominal data, 738 Inferences, 312 about difference between two means, using independent samples, 438–456 about difference between two means, using matched pairs, 464–475 about difference between two proportions, 487–498 about population proportions, 409–419 about populations, with standard deviation unknown, 386–395 about population variance, 401–407 about ratio of two variances, 481–485 definition of, 4–5 sampling distribution used for, 296–298, 308–309 for Student t distribution used for, 289 Inferential statistics, 34 Influential observations, 671–672, 706 Information types of, 13–17 used in decision analysis, 894–902 See also Data Insurance industry applications, 726 Interactions (between variables), 561, 569–570 in first-order polynomial models with two predictor variables, 731–732 in second-order polynomial models with two predictor variables, 732 sum of squares for factors and, 563–566 Intercorrelation (multicollinearity; collinearity), 707–708 Interrquartile range, 117–119 Intersections, of events, 178 Interval data, 14, 16, 368, 908 analysis of variance on, 521 calculations for, 15 graphical techniques for, 44–61 Kruskal-Wallis test for, 790–794 relationship between two interval variables, 73–78 sign test for, 777 Wilcoxon signed rank sum test for, 781–787 Interval estimators, 312–315 for population variance, 401–402 Intervals prediction intervals, 660–661, 664 width of, for confidence interval estimators, 324–325 Interval variables in multiple regression analysis, 689 relationship between two, 73–78 Interviews, 160–161 Inventory management, 278–279, 318 Investments comparing returns on, 148–149 management of, 51–52 measuring risk for, 273 mutual funds, 178– 184, 720 negative return on, 273–278 portfolio diversification and asset allocation for, 233–238 returns on, 52–54 stock market indexes for, 144–145 Joint probabilities, 178 selecting correct methods for, 206–207 Kruskal-Wallis test, 790–794 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net INDEX Laws Bayes’s Law, 196–205, 207 of expected value, 220–221, 229 of variance, 220–221, 229 Lead time, 278–279 Learning curves, 736 Least significant difference (LSD) method Bonferroni adjustment to, 542–543 Fisher’s, 540–541 Tukey’s, 543–544 Least squares line coefficients, 632–633 Least squares method, 76, 128–132, 632 Likelihood probabilities, 197, 895 Linearity, in scatter diagrams, 75–77 Linear programming, 238 Linear relationships, 123–137 coefficient of correlation for, 125–126 coefficient of determination for, 135–137 comparisons among, 127 covariance for, 124–125 least squares method for, 128 measuring strength of, 132–135 in scatter diagrams, 75–77 Line charts, 64–66 deception in, 83–87 Long-term trends, 831 Lower confidence limit (LCL), 316 Macroeconomics, 24 Marginal probabilities, 180 Marketing applications in advertising, 329 market segmentation, 423–426, 503–504, 511–512, 536, 598, 620 test marketing, 491–496, 536 Market models, 144–145 Market-related (systematic) risk, 146 Market segmentation, 423–426, 503–504, 511–512, 536, 598, 620 Markowitz, Harry, 233 Mass marketing, 423–424 Matched pairs, 549 compared with independent samples, 475–476 for inference about difference between two population means, 464–475 sign test for, 776–781 Mean of population of differences, 471 Means, approximating, for grouped data, 112 arithmetic, 95–97 of binomial distributions, 246 compared with medians, 100–101 expected values for, 219 factors involved, 103 geometric, 101–102, 103 moving averages for, 832–838 for normal distribution, 267 ordinal data not having, 760 sampling distribution of, 302–311 sampling distributions of difference between two means, 306–307 See also Population means; Sample means Mean square for treatments (mean squares; MSE), 524 for randomized block experiments, 551 Measurements descriptive, of performance, 744–745 Medians, 97–98, 761 compared with means, 100–101 factors involved, 103 used in estimate of population mean, 325–326 Medical applications comparing treatments for childhood ear infections, 585 estimating number of Alzheimer’s cases, 436 estimating total medical costs, 435–436 pharmaceutical and medical experiments, 500–501 of probability, 200– 205, 211–212 testing coronary devices, 726 Microsoft Excel See Excel Minitab, 7–8 for analysis of variance, 657, 668 for ANOVA for multiple comparisons, 540, 545–546 for arithmetic means, 97 for autoregressive forecasting model, 854 for bar and pie charts, 23, 36 for binomial distributions, 246 for box plots, 119 for χ2 (chi-squared) distribution, 295, 404–405, 406–407 for χ2 (chi-squared) goodness-of-fit test, 596 for χ2 (chi-squared) test of contingency tables, 604–605 for coefficient of correlation, 656 I-6 for coefficient of determination, 135, 652 to compute coefficients in multiple regression analysis, 690 for confidence interval estimators, 320–321 for cross-classification tables, 35 for determining probability of Type II errors, 865 for difference between two population means, 444–447, 450–452 for difference between two proportions, 493, 494, 496, 497 for Durbin-Watson test, 714, 715, 717 for exponential distribution, 285 for exponential smoothing, 841–842 for F distribution, 299 for frequency distributions, 20 for Friedman test, 797 for histograms, 48 for interactions, 569 for interpreting and testing indicator variables, 739, 742 for least squares method, 132, 136 for linear regression model, 649 for line charts, 66 for market segmentation problem, 426 for matched pairs experiments, 469, 473, 474–475 for measures of central location, 99–100 for measuring strength of linear relationships, 134 for medians, 98 missing data problem in, 414 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net I-7 INDEX for modes, 99 for moving averages, 835–836 for normal distribution, 278 for ogives, 61 for one-way analysis of variance, 527–528, 532 for pattern tests, 873–875 for pay equity problem, 748, 751 for p charts, 884 for Poisson distributions, 252 for polynomial regression analysis, 734 for power of statistical tests, 363 for prediction intervals in linear multiple regression analysis, 663–664 for prediction intervals in multiple regression analysis, 699 for p-values, 347 for quartiles, 117 for randomized block ANOVA, 554 random samples generated by, 166 for ratio of two variances, 484 for regression lines, 637 for scatter diagrams, 75 for S charts, 876 for seasonal analysis, 846–847 for sign test, 780 for Spearman rank correlation coefficient, 808 for standard deviation, 110 for standard error of estimate, 646, 691 for stem-and-leaf displays, 58–59 in stepwise regression, 754 for Student t distribution, 291 for testing population means, 353 for testing validity of multiple regression model, 694 for time-series analysis, 713 for t-statistic, 390, 392 for two-factor ANOVA, 561, 567 for two-way ANOVA, 577 for variance, 108 for Wilcoxon rank sum test, 768, 771 for Wilcoxon signed rank sum test, 787 for x charts, 872 for z scores for population proportions, 413 Missing data, 414 Mitofsky, Warren, 411n Modal classes, 50–51, 102 Model building, 755–756 Models, 630–638 building, 755–756 deterministic and probabilistic, 630–631 for forecasting, 850–856 in linear regression, assessing, 644–657 in multiple regression, 686–687 in multiple regression, assessing, 689–699 polynomial, 728–735 Modern portfolio theory (MPT), 233 Modes, 98–100 factors involved, 103 in histograms, 50–51 Moving averages, 832–838 Multicollinearity (collinearity; intercorrelation), 689, 707–708 stepwise regression and, 752 Multifactor experimental design, 549 Multinomial experiment, 592–593 Multiple comparisons ANOVA for, 538–546 Tukey’s method for, 543–544 Multiple regression analysis diagnosing violations in, 706–708 estimating coefficients and assessing models in, 689–699 models and required conditions for, 686–687 polynomial models in, 728–735 time-series data, 709–717 Multiple regression equation, 689 Multiplication rule (of probability), 188–189 Mutual funds, 178–184, 720 Mutually exclusive events, addition rule for, 191 Negative linear relationships, 76 Nominal data, 14, 17, 368, 909 calculations for, 15–16 χ2 (chi-squared) test of contingency table for, 599–607 describing a set of, 18–27 describing relationship between two nominal variables, 32–39 inferences about difference between two population proportions, 487–498 inferences about population proportions, 409–419 measures of central location for, 101 measures of variability for, 112 nominal independent variables, 737–742 tests on, 611–613 Nominal variables independent, 737–742 relationship between two, 32–39 Nonindependence of error variables, 670 of time series, 706 Nonnormal populations (nonnormality), 393 in linear regression analysis, 668 in multiple regression analysis, 706 nonparametric statistics for, 454, 477, 760–761 test of, 407 Nonparametric statistics, 760–761, 909 Friedman test, 794–798 Kruskal-Wallis test, 790–794 sign test, 776–781 Spearman rank correlation coefficient, 657, 802–808 Wilcoxon rank sum test, 454, 477 Wilcoxon signed rank sum test, 781–787 Nonresponse errors, 170 Nonsampling errors, 170 Nonsystematic (firmspecific) risk, 146 Normal density functions, 266 Normal distribution approximation of binomial distribution to, 300–302 bivariate, 643 Student t distribution as, 288 test of, 407 Normal distributions, 266–276 Normality, χ2 (chi-squared) test for, 613–616 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net INDEX Normal random variables, 266 Null hypothesis, 335–338, 908 calculating probability of Type II errors and, 359–366 determining alternative hypothesis for, 365–366 for Friedman test, 794 for Kruskal-Wallis test and Friedman test, 791 in nonparametric statistics, 761, 772 for sign test, 777–778, 791 in statistical process control, 862–863 Numerical descriptive techniques baseball applications of, 140–144 financial applications of, 144–146 graphical descriptive techniques compared with, 148–149 for measures of central location, 95–103 for measures of linear relationship, 123–137 for measures of relative standing and box plots, 114–122 for measures of variability, 105–112 Observation, 159–160 Observational data, 464–466 error variables for, 643 experimental data and, 476 influential observations, 671–672, 706 Observed frequencies, 594 Ogives, 59–61 One-sided confidence interval estimators, 354 One-tailed tests, 350–351, 353–354 for linear regression model, 650 One-way analysis of variance, 520–533 Operating characteristic (OC) curve, 364–365, 864 Operations management applications finding and reducing variation, 573–578 inventory management in, 278–279, 318 learning curve, 736 location analysis, 704–705 pharmaceutical and medical experiments, 500–501 Project Evaluation and Review Technique and Critical Path Method in, 231–233, 282 quality of production in, 402 waiting lines in, 253–254, 285 Opportunity loss, 889 Ordinal data, 14–15, 368, 909 calculations for, 16 describing, 27 Kruskal-Wallis test for, 790–794 measures of central location for, 101 measures of relative standing for, 121–122 measures of variability for, 112 nonparametric statistics for, 760–761 Outliers, 118 in linear regression analysis, 671 in multiple regression analysis, 706 Parameters, 159 in Bayesian statistics, 902 definition of, 4, 95 Paths (in operations management), 231–232 Pattern tests, 872–875, 882–884 Pay equity, 746–751 Payoff tables, 889 p charts, 882–884 Pearson coefficient of correlation (Pearson correlation coefficient), 654, 803 Percentiles, 114–116 definition of, 114 Performance measurement, 744–745 Personal interviews, 160–161 Pharmaceutical and medical experiments, 500–501 Pictograms, 86 Pie charts, 21–25 Point estimators, 312–315 Point prediction, 660 Poisson distributions, 248–252 Poisson experiments, 248 Poisson probability distributions, 249 Poisson random variables, 248, 249 Poisson tables, 250–252 Polls errors in, 163 exit polls, 4, Polynomial models, 728–735 Pooled proportion estimate, 489 Pooled variance estimators, 440 Population means analysis of variance test of differences in, 520 estimating, using sample median, 325–326 estimating, with standard deviation known, 315–326 expected values for, 219 I-8 inferences about differences between two, using independent samples, 438–456 inferences about differences between two, using matched pairs, 464–475 testing, when population standard deviation is known, 339–355 Populations, 368–369 coefficient of correlation for, 125 covariance for, 124 definition of, 4, 13 inferences about, with standard deviation unknown, 386–395 inferences about population proportions, 409–419 large but finite, 394 nonnormal, 393 nonparametric statistics for, 760–761 probability distributions and, 218–220 in sampling distribution of mean, 302–303 target and sampled, 163 variance for, 105 Populations standard deviations, 219 Population variance, 219 inferences about, 401–407 Portfolio diversification, 233–238 Positive linear relationships, 76 Posterior probabilities (revised probabilities), 197, 896 Power of statistical tests, 362 Excel and Minitab for, 363 Prediction intervals in linear regression analysis, 660–661, 663–664 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net I-9 INDEX in multiple regression analysis, 698–699 Predictor variables in model building, 756 in polynomial models, 728–732 Preposterior analysis, 898–899 Prevention approach (to quality control), 860 Prior probabilities, 197, 896 Probabilistic models, 630–631 Probability assigning for events, 173–176 Bayes’s Law for, 196–205 joint, marginal, and conditional, 177–184 in normal distribution, calculating, 267–269 rules of, 188–192 selecting correct methods for, 206–207 trees to represent, 192–194 Probability density functions, 260–265 exponential, 283 Probability distributions, 214–221 binomial, 242 definition of, 215 Poisson, 249 populations and, 218–220 Probability trees, 192–194 Process capability index, 574, 879 Process control See Statistical process control Process variance, 879 Process variation, 860–861 Project Evaluation and Review Technique (PERT), 231–233, 282 Proportions inferences about difference between two population proportions, 487–498 inferences about population proportions, 409–419 sampling distribution of, 300–304 Prostate cancer, 200–205 p-values, 342–343 definition of, 343 Excel and Minitab for, 345–347 interpreting, 343–345 Quadratic relationships, 647 Quality control See Statistical process control Quartiles, 115–117 Questionnaires, design of, 161–162 Random-effects analysis of variance, 550 Random experiments, 173–174 Randomized block (twoway) analysis of variance, 550–557 Randomized block design, 549 Random sampling cluster sampling, 168–169 simple, 164–166 stratified, 166–168 Random variables, 214–221 binomial, 241, 242 definition of, 215 exponential, 284 exponential probability density function for, 283 normal, 266 Poisson, 248, 249 standard normal random variables, 267–269 Random variation, 832 Range, 2, 105 interrquartile range, 117–119 Ranks Friedman test for, 794–798 Kruskal-Wallis test for, 791–794 Spearman rank correlation coefficient for, 802–808 Wilcoxon rank sum test for, 762–772 Wilcoxon signed rank sum test for, 781–787 Ratios, of two variances, 481–485 Rectangular probability distributions (uniform probability density functions), 262–265 Regression analysis, 629–630 diagnosing violations in, 666–673 equation for, 660–664 estimation of coefficients in, 632–638 fitting regression lines in, 634 model building procedure, 755–756 models in, 630–638 multiple, 686–687 nominal independent variables in, 737–742 for pay equity problem, 748–751 polynomial models in, 728–735 stepwise regression, 752–755 time-series data, 709–717 See also First-order linear model; Multiple regression analysis Regression equation, 660–664, 698–699 Regression lines, Excel and Minitab for, 637 Rejection region, 339–341 for χ2 (chi-squared) test of contingency tables, 603 definition of, 340 one- and two-tailed tests, 350–351, 353–354 p-values and, 345 z scores for, 341–342 Relative efficiency, 314 Relative frequency approach, in assigning probabilities, 175 Relative frequency distributions, 18, 59 Relative standing, measures of, 114–122 Reorder points, 279 Repeated measures, 549 Replicates, 563 Research hypothesis (alternative hypothesis), 335–338, 348 determining, 365–366 Residual analysis, 666–668 Residuals in linear regression analysis, 666–668 in sum of squares for error, 634 Response rates, to surveys, 160 Responses, 522 Response surfaces, 688 Response variable, 522 Return on investment, 52–54 investing to maximize, 236–237 negative, 273–278 Revised probabilities (posterior probabilities), 197, 896 ρ (rho), for coefficient of correlation, 125 Risks investing to minimize, 236–237 market-related and firm-specific, 146 measuring, 273 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net INDEX Robustness of test statistics, 393 Rollback technique, for decision trees, 892 Rule of five, 596, 605 Safety stocks, 279 Sampled populations, 163 Sample means as estimators, 312 as test statistics, 338 x charts of, 862–863, 868–875 Samples coefficient of correlation for, 125 covariance for, 124 definition of, 4, 13 exit polls, independent, 549 matched pairs compared with independent samples, 475–476 missing data from, 414 size of, 168 variance for, 105–108 Sample size, 168, 329–331 barmonic mean of, 544 to estimate proportions, 416–418 increasing, 361–362 in statistical process control, 864–865 Sample space, 174 Sample variance, 394 Sampling, 162–170 acceptance sampling, 887–888, 900–901 errors in, 169–170 frequency of, in statistical process control, 864–865 replacement in selection of, 189–190 sample size for, 329–331 simple random sampling for, 164–166 Sampling distributions, 908 derivation of formula, 370 of difference between two means, 306–307 for differences between two population means, 439 inferences from, 308–309 for linear regression models, 647–648 of means, 302–311 of means of any population, 306–307 for one-way ANOVA, 525 of proportions, 300–304 of sample means, 304–305, 307 of sample proportion, 304 Sampling errors, 169–170 Sampling frequency, 864–865 Sampling plans, 908 Scatter diagrams, 73–78 compared with other measures of linear relationship, 127 S charts, 875–878 Screening tests for Down syndrome, 211–212 for prostate cancer, 200–205 Seasonal analysis, 843–844 Seasonal effects, 843–848 Seasonal indexes, 851–852 Seasonally adjusted time series, 847 Seasonal variation, 832 Second-order polynomial models, 729 with interaction, 732 Secular trends, 831 Selection, with and without replacement, 189–190 Selection bias, 170 Self-administered surveys, 161 Self-selected samples, 163 Serially correlated (autocorrelated) error variables, 670 σ2 (sigma squared) calculation shortcut, 219 for population variance, inferences about, 401–402 ratio of two population variances, 438, 481–483 for sample variance, 105–107 Significance levels, 4–5 p-values and, 345 for Type I errors, 335 Sign test, 776–777 factors involved, 787 Friedman test and, 798 Simple events, 175–176 Simple linear regression model See Firstorder linear model Simple random sampling, 164–166 cluster sampling, 168–169 definition of, 164 Single-factor experimental design, 549 Six sigma (tolerance goal), 574 Skewness, in histograms, 50 Slope, in linear regression analysis, 646–647 Smith, Adam, 93 Smoothing techniques exponential smoothing, 838–842, 851 moving averages, 832–838 Spearman rank correlation coefficient, 657, 802–808 Special (assignable) variation, 860 Specification limits, 861 Spreadsheets, 7–8 See also Excel I-10 Stacked data format, 454–455 Standard deviations, 109–112 Chebysheff’s Theorem for, 111 estimating population mean, with standard deviation known, 315–326 for normal distribution, 267 populations standard deviations, 219 of residuals, 667–668 of sampling distribution, 306 testing population mean, when population standard deviation is known, 339–355 t-statistic estimator for, 387 Standard error of estimate in linear regression analysis, 645 in multiple regression analysis, 691–692, 694–695 Standard errors of difference between two means, 304 of estimates, 645 of mean, 306 of proportions, 304 Standardized test statistics, 341–342 Standard normal random variables, 267–269 States of nature, 888 Statistical inference, 302, 312 definition of, 4–5 sampling distribution used for, 296–298, 308–309 for Student t distribution used for, 289 Statistical process control (SPC; quality control), 860 control charts for attributes in, 882–884 control charts for variables in, 868–879 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net I-11 INDEX control charts in, 862–867 process variation, 860–861 Statisticians, 1–2n Statistics applications in business, concepts for life in, 908–909 definition of, 1–2, 5, 95 descriptive, 2–3 inferential, 3–5 of samples, Stem-and-leaf displays, 57–59 Stepwise regression, 752–755 Stocks and bonds portfolio diversification and asset allocation for, 233–238 stock market indexes, 144–145 valuation of, 51–52 Stratified random sampling, 166–168 Student t density function, 288 t-statistic and, 387 Student t distribution, 286–291, 394 for difference between two population means, 440 for nonnormal populations, 393 table for, 289 t-statistic and, 387 Subjective approach, in assigning probabilities, 175 Sum of squares for blocks (SSB), 551, 555 for error (SSE), 634, 644 for error (withintreatments variation; SSE) for one-way ANOVA, 523–524 for factors and interactions, 563–566 for forecast errors (SSE), 849–850 for treatments (between-treatments variation; SST) for one-way ANOVA, 522–523 Surveys, 160–162 missing data from, 414 Symmetry, in histograms, 50 Systematic (marketrelated) risk, 146 Taguchi, Genichi, 575 Taguchi loss function, 575–576 Target populations, 163 Taxes, auditing, 172, 199 t distribution See Student t distribution Telephone interviews, 161 Testing, false positive and false negative results in, 200–205 Test marketing, 491–496, 536 Test statistics, 338 standardized, 341–342 t-statistic, 387 Third-order polynomial models, 729–730 Time-series analysis components of, 831–832 exponential smoothing for, 838–842 forecasting and, 850–851 forecasting models for, 851–856 moving averages for, 832–838 trends and seasonal effects in, 843–848 Time-series data, 64–68, 830 deseasonalizing, 847–848 diagnosing violations in, 709–717 Time-series forecasting, 830 Tolerance, in variation, 573–574 Taguchi loss function for, 575–576 Treatment means (in ANOVA), 520 Treeplan, 892 Trend analysis, 843 Trends (secular trends), 831 in process variation, 861 seasonal effects and, 843–848 type of variation in, 832 t-statistic, 387–389, 394–395 Excel and Minitab for, 389–390, 392 F-statistic and, 530–531 variables in, 395 t-tests analysis of variance compared with, 529–530 coefficient of correlation and, 654, 655 Excel for, 395 for matched pairs experiment, 468–469 multicollinearity and, 752 for multiple regression analysis, 698 for observational data, 465 for two samples with equal variances, 455 for two samples with unequal variances, 456 Tufte, Edward, 82 Tukey, John, 57 Tukey’s least significant difference (LSD) method, 543–544, 546 Two-factor analysis of variance, 559–571 Two-tailed tests, 350–351, 353–354 Two-way (randomized block) analysis of variance, 550–557 Type I errors, 335–336, 908 determining alternative hypothesis for, 365–366 in multiple regression analysis, 689, 698 multiple tests increasing chance of, 529, 541 relationship between Type II errors and, 361 in statistical process control, 863 Type II errors, 335, 908 calculating probability of, 359–366 determining alternative hypothesis for, 365–366 in statistical process control, 863–866 Unbiased estimators, 313–314 Unequal-variances test statistic, 441 estimating difference between two population means with, 451–452 Uniform probability density functions (rectangular probability distributions), 262–265 Unimodal histograms, 50–51 Union, of events, 183 addition rule for, 190–192 Univariate distributions, 225 Univariate techniques, 32 Unstacked data format, 454 Upper confidence limit (UCL), 316 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net INDEX Validity of model, testing, 693–695 Valuation of stocks and bonds, 51–52 Values, definition of, 13 Variability, measures of, 2, 105–112 coefficient of variation, 112 range, 105 standard deviations, 109–112 variance, 105–109 Variables, 369 control charts for, 867–879 definition of, 13 dependent and independent, 629 interactions between, 561, 569–570 nominal, describing relationship between two nominal variables, 32–39 nominal independent, 737–742 in one-way analysis of variance, 522 predictor, 728–732 random, 214–221 time-series, 830 types of, 17 Variance, 105–109 approximating, for grouped data, 112 of binomial distributions, 246 estimating, 313–314 inferences about ratio of two variances, 481–485 interpretation of, 108–109 Law of, 220–221 in matched pairs experiments, 475 pooled variance estimators for, 440 population variance, 219, 401–407 in sampling distribution of mean, 303 shortcut for, 107–108 Variation, 860 coefficient of, 112 finding and reducing, 573–578 process variation, 860–861 types of, in time-series data, 832 Voting and elections electoral fraud in, 155–156 errors in polls for, 163 exit polls in, 4, 411 Waiting lines, 253–254, 285 Wilcoxon rank sum test, 454, 477, 762–772 Kruskal-Wallis test and, 794 Wilcoxon signed rank sum test, 781–787 Wilson, Edwin, 418 Wilson estimators, 418–419 I-12 Within-treatments variation (SSE; sum of squares for error), for one-way ANOVA, 523–524 x charts, 862–863, 868–875 used with S charts, 877 z scores (z tests), 267–279 for difference between two proportions, 492–494, 497 finding, 273–278 of nominal data, 612 for population proportions, 412–413 for standardized test statistic, 341–342 table of, 270 z-statistic, 395 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net APPLICATION BOXES Accounting Breakeven analysis Fixed and variable costs Introduction 128 Least squares line to estimate fixed and variable costs 129 Economics Macroeconomics Energy economics Measuring inflation Demand curve Introduction 24 Pie chart of sources of energy in the United States 24 Removing the effect of inflation in a time series of prices Polynomial model to relate price and demand 735 66 Finance Stock and bond valuation Return on investment Geometric mean Stock market indexes Mutual funds Measuring risk Introduction 51 Histograms of two sets of returns to assess expected returns and risk 52 Calculating average returns on an investment over time 101 Introduction to the market model 144 Marginal and conditional probability relating mutual fund performance with manager’s education 178 Normal distribution to show why the standard deviation is a measure of risk 273 Human Resource Management Employee retention Job applicant testing Severance pay Performance measure Regression analysis to predict which workers will stay on the job 640 Regression analysis to determine whether testing job applicants is effective 641 Multiple regression to judge consistency of severance packages to laid-off workers 701 Multiple regression to predict absenteeism 744 Marketing Pricing Advertising Test marketing Market segmentation Market segmentation Test marketing Market segmentation Market segmentation Market segmentation Histogram of long-distance telephone bills 44 Estimating mean exposure to advertising 342 Inference about the difference between two proportions of product purchases 491 Inference about two proportions to determine whether market segments differ 503 Inference about the difference between two means to determine whether two market segments differ 511 Analysis of variance to determine differences between pricing strategies 536 Analysis of variance to determine differences between segments 536 Chi-squared goodness-of-fit test to determine relative sizes of market segments 598 Chi-squared test of a contingency table to determine whether several market segments differ 620 Operations Management PERT/CPM Waiting lines Inventory management PERT/CPM Waiting lines Inventory management Quality Pharmaceutical and medical experiments Location analysis Learning curve Expected value of the completion time of a project 231 Poisson distribution to compute probabilities of arrivals 253 Normal distribution to determine the reorder point 278 Normal distribution to determine the probability of completing a project on time 282 Exponential distribution to calculate probabilities of service completions 285 Estimating mean demand during lead time 331 Inference about a variance 402 Inference about the difference between two drugs 500 Multiple regression to predict profitability of new locations 704 Polynomial model to relate analyze productivity and job tenure 736 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Index of Computer Output and Instructions Techniques General Data input and retrieval Recoding data Stacking/Unstacking data Excel Minitab CD App A1 CD App B1 CD App N CD App N CD App R CD App R Graphical Frequency distribution Bar chart Pie chart Histogram Stem-and-leaf display Ogive Line chart Pivot table Cross-classification table Scatter diagram Box plot 20 22 22 47 58 60 65 34 34 74 118 20 23 23 48 58 — 66 — 35 75 119 Numerical descriptive techniques Descriptive statistics Least squares Correlation Covariance Determination 100 131 133 133 135 100 132 134 134 135 Probability/random variables Binomial Poisson Normal Exponential Student t Chi-squared F 245 252 277 285 291 295 298 246 252 278 285 291 295 298 Inference abou T μ ( σ known) Interval estimator Test statistic Probability of Type II error 332 359 376 332 359 376 Inference about μ ( σ unknown) Test statistic Interval estimator 389 392 390 392 Inference about σ2 Test statistic Interval estimator 409 406 404 406 Inference about P Test statistic Interval estimator 412 415 413 415 Inference about μ1 − μ2 Equal-variances test statistic Equal-variances interval estimator Unequal-variances test statistic Unequal-variances interval estimator 443 445 449 450 444 445 449 451 Techniques Excel Minitab Inference about μ D Test statistic Interval estimator 472 474 473 474 Inference about σ21/σ22 Test statistic Interval estimator 483 485 484 — Inference about p1 − p2 Test statistic Interval estimator 492 494 493 494 Analysis of variance One-way Multiple comparison methods Two-way Two-factor 527 539 553 566 527 540 554 567 Chi-squared tests Goodness-of-fit test Contingency table Test for normality 595 604 615 596 604 — Linear regression Coefficients and tests Correlation (Pearson) Prediction interval Regression diagnostics 637 656 663 667 637 656 663 668 Multiple regression Coefficients and tests Prediction interval Durbin-Watson test Stepwise regression 690 669 714 753 690 669 714 754 Nonparametric techniques Wilcoxon rank sum test Sign test Wilcoxon signed rank sum test Kruskal-Wallis test Friedman test Spearman rank correlation 767 780 786 793 797 808 768 780 787 793 797 808 Forecasting and time-series analysis Moving averages 835 Exponential smoothing 840 Seasonal indexes 846 835 841 846 Statistical process control x chart S chart P chart 872 876 884 871 876 883 Copyright 2014 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it ... computer, Statistics for Management and Economics is designed for maximum flexibility and ease of use for both instructors and students To this end, parallel illustration of both manual and computer... statistical concepts and their applications to the real world Statistics for Management and Economics is designed to demonstrate that statistics methods are vital tools for today’s managers and economists... experiment Column 1: ID number the number of episodes, num- One hundred and eighty children Column 2: Group number ber of physician visits, number between 10 months and years Column 3: Number of episodes