Business statistics a decision making approach 10th global edtion by groebner

Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner Tài liệ Business statistics a decision making approach 10th global edtion by groebner

BUSINESS Statistics A Decision-Making Approach A01_GROE0383_10_GE_FM.indd 29/08/17 5:15 PM This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM TENTH EDITION GLOBAL EDITION BUSINESS Statistics A Decision-Making Approach David F Groebner Boise State University, Professor Emeritus of Production Management Patrick W Shannon Boise State University, Professor Emeritus of Supply Chain Management Phillip C Fry Boise State University, Professor of Supply Chain Management Harlow, England • London • New York • Boston • San Francisco • Toronto • Sydney • Dubai • Singapore • Hong Kong Tokyo • Seoul • Taipei • New Delhi • Cape Town • Sao Paulo • Mexico City • Madrid • Amsterdam • Munich • Paris • Milan A01_GROE0383_10_GE_FM.indd 29/08/17 5:15 PM Director, Portfolio Management: Deirdre Lynch Portfolio Management Assistants: Justin Billing and Jennifer Snyder Associate Acquisitions Editor, Global Edition: Ananya Srivastava Associate Project Editor, Global Edition: Paromita Banerjee Content Producer: Kathleen A Manley Content Producer, Global Edition: Isha Sachdeva Senior Manufacturing Controller, Global Edition: Kay Holman Managing Producer: Karen Wernholm Media Producer: Jean Choe Manager, Courseware QA: Mary Durnwald Manager, Content Development: Robert Carroll Product Marketing Manager: Kaylee Carlson Product Marketing Assistant: Jennifer Myers Senior Author Support/Technology Specialist: Joe Vetere Manager, Media Production, Global Edition: Vikram Kumar Text Design, Production Coordination, Composition: Cenveo® Publisher Services Illustrations: Laurel Chiapetta and George Nichols Cover Design, Global Edition: Lumina Datamatics Cover Image: Tashatuvango/Shutterstock Acknowledgements of third party content appear on page 859–860, which constitutes an extension of this copyright page PEARSON, ALWAYS LEARNING, and PEARSON MYLAB STATISTICS are exclusive trademarks owned by Pearson Education, Inc or its affiliates in the U.S and/or other countries Pearson Education Limited KAO Two KAO Park Harlow CM17 9NA United Kingdom and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearsonglobaleditions.com © Pearson Education Limited 2018 The rights of David F Groebner, Patrick W Shannon, and Phillip C Fry to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 Authorized adaptation from the United States edition, entitled Business Statistics: A Decision-Making Approach, 10th Edition, ISBN 978-0-13-449649-8 by David F Groebner, Patrick W Shannon, and Phillip C Fry, published by Pearson Education © 2018 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby Street, London EC1N 8TS All trademarks used herein are the property of their respective owners The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affiliation with or endorsement of this book by such owners ISBN 10: 1-292-22038-4 ISBN 13: 978-1-292-22038-3 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library 10 Typeset in Times Lt Pro by Cenveo Publisher Services Printed and bound by Vivar in Malaysia A01_GROE0383_10_GE_FM.indd 11/09/17 1:50 PM To Jane and my family, who survived the process one more time david f groebner To Kathy, my wife and best friend; to our children, Jackie and Jason patrick w shannon To my wonderful family: Susan, Alex, Allie, Candace, and Courtney phillip c fry A01_GROE0383_10_GE_FM.indd 29/08/17 5:15 PM This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM About the Authors David F Groebner, PhD, is Professor Emeritus of Production Management in the College of Business and Economics at Boise State University He has bachelor’s and master’s degrees in engineering and a PhD in business administration After working as an engineer, he has taught statistics and related subjects for 27 years In addition to writing textbooks and academic papers, he has worked extensively with both small and large organizations, including Hewlett-Packard, Boise Cascade, Albertson’s, and Ore-Ida He has also consulted for numerous government agencies, including Boise City and the U.S Air Force Patrick W Shannon, PhD, is Professor Emeritus of Supply Chain Operations Management in the College of Business and Economics at Boise State University He has taught graduate and undergraduate courses in business statistics, quality management and lean operations and supply chain management Dr Shannon has lectured and consulted in the statistical analysis and lean/quality management areas for more than 30 years Among his consulting clients are Boise Cascade Corporation, Hewlett-Packard, PowerBar, Inc., Potlatch Corporation, Woodgrain Millwork, Inc., J.R Simplot Company, Zilog Corporation, and numerous other public- and private-sector organizations Professor Shannon has co-authored several university-level textbooks and has published numerous articles in such journals as Business Horizons, Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research He obtained BS and MS degrees from the University of Montana and a PhD in statistics and quantitative methods from the University of Oregon Phillip C Fry, PhD, is a professor of Supply Chain Management in the College of Business and Economics at Boise State University, where he has taught since 1988 Phil received his BA and MBA degrees from the University of Arkansas and his MS and PhD degrees from Louisiana State University His teaching and research interests are in the areas of business statistics, supply chain management, and quantitative business modeling In addition to his academic responsibilities, Phil has consulted with and provided training to small and large organizations, including Boise Cascade Corporation, Hewlett-Packard Corporation, the J.R Simplot Company, United Water of Idaho, Woodgrain Millwork, Inc., Boise City, and Intermountain Gas Company A01_GROE0383_10_GE_FM.indd 29/08/17 5:15 PM This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM Brief Contents The Where, Why, and How of Data Collection   25 Graphs, Charts, and Tables—Describing Your Data   Describing Data Using Numerical Measures   97 1–3 SPECIAL RE VIE W SECTION   10 11 12 146 Introduction to Probability   152 Discrete Probability Distributions   196 Introduction to Continuous Probability Distributions   236 Introduction to Sampling Distributions   263 Estimating Single Population Parameters   301 Introduction to Hypothesis Testing   340 Estimation and Hypothesis Testing for Two Population Parameters   Hypothesis Tests and Estimation for Population Variances   434 Analysis of Variance   458 8–12 SPECIAL RE VIE W SECTION   13 14 15 16 17 18 19 20 52 387 505 Goodness-of-Fit Tests and Contingency Analysis   521 Introduction to Linear Regression and Correlation Analysis   550 Multiple Regression Analysis and Model Building   597 Analyzing and Forecasting Time-Series Data   660 Introduction to Nonparametric Statistics   711 Introducing Business Analytics   742 Introduction to Decision Analysis  (Online) Introduction to Quality and Statistical Process Control  (Online) A P P E N D IC E S A Random Numbers Table  768 B Cumulative Binomial Distribution Table  769 C Cumulative Poisson Probability Distribution Table  783 D Standard Normal Distribution Table  788 E Exponential Distribution Table  789 F Values of t for Selected Probabilities  790 G Values of x2 for Selected Probabilities  791 H F-Distribution Table  792 I Distribution of the Studentized Range (q-values)  798 J Critical Values of r in the Runs Test  800 K Mann–Whitney U Test Probabilities (n * 9)  801 L Mann–Whitney U Test Critical Values (9 " n " 20)  803 M Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n N Critical Values dL and dU of the Durbin-Watson Statistic D  806 O Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test  808 P Control Chart Factors  809 A01_GROE0383_10_GE_FM.indd " 25)  805 30/08/17 3:52 PM www.downloadslide.net Index 851 class width, 60 coefficient of determination, 572 coefficient of variation, 130 complement rule, 168 conditional probability, 173, 176 confidence interval, 305, 326, 388–389, 391, 440, 581, 611 correction for tied rankings, 733 correlation coefficient, 552, 602 counting rule for combinations, 207 deflation, 666 degrees of freedom, 394, 407 density functions, 238, 251, 253 deseasonalization, 686 Durbin-Watson statistic, 673 estimated regression model, 564, 599 expected cell frequencies, 540 expected value, 199, 211 exponential probability, 253 exponential smoothing, 691, 695 Fisher’s least significant difference, 484 forecast bias, 677 F-test, 444, 605 H-statistic, 731 hypergeometric distribution, 223, 225 hypothesis testing, 347, 381 interquartile range, 120 least squares, 564, 669 linear trend, 669 margin of error, 308, 328 mean, 252, 289 mean absolute deviation, 672 mean square error, 672 median index point, 103 multiple coefficient of determination, 604 multiplication rule, 177, 179 multiplicative time-series model, 683 paired difference, 411–412 partitioned sum of squares, 461, 479 percentile location index, 110 point estimate, 412 Poisson distributions, 218 polynomial regression model, 622–625 pooled estimator, 421 population mean, 98, 265 population multiple regression model, 598 population proportion, 286 population standard deviation, 122 population variances, 121, 122 power of the test, 374, 381 prediction interval for y, given x, 582 range, 119 ratio-to-moving-average, 684 relative frequency, 56, 159 residuals, 566, 643 sample error, 264 sample mean, 102, 265 sample proportion, 287, 325 sample size, 320, 328 sample standard deviation, 124 sample variance, 124, 446 simple index number, 665 simple linear regression, 560 Z05_GROE0383_10_GE_IDX.indd 851 single-proportion sampling error, 287 standard deviation, 199, 212, 220, 252, 412 standard error, 289, 326, 608 standardized normal z-value, 239 standardized residual, 647 sum of squares between, 464 sum of squares blocking, 479 sum of squares error, 566 sum of squares regression, 572 sum of squares within, 465, 479 t-distribution, 311 total sum of squares, 464, 571 t-test statistic, 355, 381, 401, 414, 554, 607 Tukey-Kramer critical range, 470 two-factor ANOVA, 491 U-statistics, 719, 720 variance inflation factor, 610 weighted mean, 108 Wilcoxon mean and standard deviation, 724 Wilcoxon test statistic, 714, 725 z-test statistic, 348, 363, 381, 399, 421 z-value for sampling distribution of mean, 278 Errors forecasting, 670–671 margin of error, 307–309, 328 mean square, 465, 490, 671, 672 measurement, 36 sampling See Sampling error standard See Standard error statistical, 344–345 sum of squares See Sum of squares error (SSE) type I, 344–345, 368 type II, 344–345, 368–374 Estimated multiple regression model, 599 Estimates confidence interval See Confidence interval estimates critical value in, 304 difference between two population means, 388–395 hypothesis testing flow diagram for, 427 objectives of, 28 paired samples, 411–413 point, 302, 303 population proportion, 325–330, 419–420 sample size, 319–322, 328–330 single population variance, 435–441 statistical, 301 Events defined, 155 dependent, 157–158 independent, 157–158, 176–177, 179 mutually exclusive, 156–157, 172–173 sample space, 154 Excel worksheets best subsets regression, 639–640 binomial distributions, 211 confidence interval, 314 contingency analysis, 540 correlation coefficient, 553 correlation matrix, 636 descriptive data, 27, 108 dummy variables, 617 Durbin-Watson statistic, 673, 675 exponential probability distributions, 254 exponential smoothing, 692–693, 696–697 forecasting, 698–701 forward selection procedure, 637 frequency distributions, 57, 58 F-test, 449 goodness-of-fit tests, 527–528 histograms, 64, 107, 132, 303 hypothesis testing, 358, 403–405 joint frequency distributions, 69, 70 Kruskal–Wallis one-way ANOVA, 732 linear trend forecasting, 668, 670 line charts, 84–85 multiple regression analysis, 602, 604, 611, 617 nonlinear trend, 663 nonlinear trend forecasting, 678–681 normal distributions, 245, 247 one-way ANOVA, 468–469, 472 Poisson distributions, 221 polynomial regression model, 625, 626, 627–628 population mean, 101 randomized complete block ANOVA, 480 random numbers table, 40 regression analysis, 579 residuals, 671 sampling distribution of mean, 273 scatter diagrams/scatter plots, 87, 553, 568 seasonal indexes, 683–685 simple linear regression, 565–566, 567, 568, 570, 624 sum of squares error, 630–631 two-factor ANOVA, 492–494 Exit polls, 36 Expected cell frequencies, 540 Expected frequency, 524 Expected value of binomial distributions, 211–212 defined, 199 of discrete probability distributions, 199, 200–201 equations, 199, 211 Experimental design, 30 Experiments, 29, 30, 154 Exponential distribution, 252–254 Exponential smoothing, 691–698 defined, 691 double, 694–698 equations for, 691, 695 Excel worksheet and instructions, 692–693, 696–697 single, 691–694 External validity, 36 F Factor, defined, 459 Finite population correction factor, 278 Fisher’s least significant difference test, 484–485 26/08/17 2:58 PM 852 www.downloadslide.net Index Fixed effects, 473 Forecasting See also Time-series data bias in, 677 defined, 661 errors, 670–671 Excel worksheet and instructions, 698–701 exponential smoothing, 691–698 model-building process for, 661 qualitative, 661 quantitative, 661 residuals, 670–671 seasonally unadjusted, 687 time-series data, 662–665 trend-based See Trend-based forecasting true forecasts, 676–677 Forecasting horizon, 661 Forecasting interval, 662 Forecasting period, 661 Forward selection procedure, 635–637 Frequency distributions, 53–62 applications of, 57, 58–60 classification of data in, 59–60 continuous variables, 57, 61–62 cumulative, 61 data array in, 59 defined, 53 discrete data as starting point for, 53, 54 Excel worksheet and instructions, 57, 58 grouped data, 57–62 joint, 67–70, 69 qualitative, 56, 57 quantitative, 56 relative, 55–56, 61 table, 53, 54–55 Frequency histograms construction of, 64–65 defined, 62 Excel worksheet and instructions, 107 relative, 65–67 F-test assumptions regarding, 444 coefficient of determination, 572–573 equation for F-test statistic, 444, 605 Excel worksheet and instructions, 449 multiple regression, 605 partial, 629–631 two population variances, 445–451 Full regression, 635 G Goodness-of-fit tests, 522–532 applications for, 401, 528–532 chi-square, 245, 522–527 degrees of freedom, 524, 528, 529, 530 Excel worksheet and instructions, 527–528 sample size in, 524 Graphs See also Charts importance for business, 27–28, 52–53, 97 ogives, 65, 67 power curve, 375 Grouped data frequency distributions, 57–62 applications of, 58–60 classification of data in, 59–60 Z05_GROE0383_10_GE_IDX.indd 852 population proportion, 362–365, 420–423 population variances, 444–451 power curve in, 375 power of the test, 374–375, 381 procedural decision-making in, 380 p-value, 351–352, 353–354, 401 research hypothesis, 342–343 significance level, 345–346 single population variance, 435–441 statistical, 341 statistical errors in, 344–345 status quo, 342 t-test statistic in, 355–359, 401–403, 405–407 two population means using independent samples, 398–407 two-tailed tests, 352, 353–355 Type I errors in, 344–345, 368 Type II errors in, 344–345, 368–374 types of, 352–355 z-test statistic in, 348–351, 399–400 continuous variables, 57, 61–62 cumulative, 61 data array in, 59 relative, 61 H Hierarchy of data, 44 Histograms, 62–67 bar charts vs., 75 construction of, 63–64 empirical rule, 132 examples of, 27 Excel worksheets and instructions, 64, 107, 132, 303 frequency, 62, 64–67, 107 information displayed in, 62–63 quantitative data, 62 relative frequency, 65–67 residuals, 648 Holdout data, 676 Homogeneous strata, 40 Horizontal bar charts, 74 H-statistic, 731, 733 Hypergeometric distributions, 221–226 applications of, 197, 221–223 defined, 221, 222 equations for, 223, 225 multiple possible outcomes per trial, 224–226 two possible outcomes per trial, 223–224 Hypotheses alternative, 341, 343–344 ANOVA, 460, 461 formulating, 341–344 Mann–Whitney U-test, 718–720 null, 341, 343–344, 353–354, 460, 461 population variances, 436–437 research, 342–343 Hypothesis testing, 340–375 alternative hypothesis, 341, 343–344 calculating beta in, 368–370 chi-square tests, 436–437 claim about population, 343 coefficient of determination, 572 conducting, 347–348 controlling alpha and beta in, 370 correlation coefficient, 554–555 critical value in, 346–348 decision rules in, 348–351 difference between two population proportions, 420–423 equations in, 347, 381 Excel worksheet and instructions, 358, 403–405 flow diagram for estimates, 427 F-test, 444–451 means, 341–359 multiple regression analysis, 605 null hypothesis, 341, 343–344, 353–354 objectives of, 28 one-tailed tests, 349–350, 352 paired samples, 414–416 population mean, 355–359, 398–407 I Independent events, 157–158, 176–177, 179 Independent samples confidence interval estimates for, 389, 391–393 defined, 388, 398, 444 estimating difference between two population means using, 388–395 hypothesis testing for two population means using, 398–407 Independent variables, 86, 551, 598, 614 Index numbers, 665–667 Inferential procedures, 26, 28 See also Estimates; Hypothesis testing Interaction cautions regarding, 494 defined, 626 in nonlinear relationships, 625–628 partial F-test for, 629–631 two-factor ANOVA, 490–494 Internal validity, 36 Interquartile range, 120–121 Interval data, 45 Interviewer bias, 35 Interviews, 29, 34 J Joint frequencies, 170 Joint frequency distributions, 67–70, 69 Joint probability, 173 Joint relative frequency tables, 69, 70 Judgment sampling, 39 K Key performance indicators (KPIs), 744 Kruskal–Wallis one-way ANOVA, 463, 729–733 L Leading questions, 33 Least significant difference (LSD), 484–485 26/08/17 2:58 PM www.downloadslide.net Index 853 Least squares criterion defined, 562 equations for, 564, 669 regression properties, 566–568 Left-skewed data, 104, 105 Levels, defined, 460 Linear relationships, 551 Linear trend forecasting, 668–670 Linear trends, defined, 663 Line charts, 83–85 Location measures Mean, 98–115 Median, 103–115 mode, 105–107, 115 percentiles, 109–111 quartiles, 111 LSD (least significant difference), 484–485 M MAD (mean absolute deviation), 671, 672 Mann–Whitney U-test, 717–722 applications of, 717–720 assumptions regarding, 717 critical value, 719–720 hypotheses, 718–720 large samples, 720–722 U-statistics, 719, 720 Marginal frequencies, 170, 535 Marginal probability, 173 Margin of error confidence interval, 307–309 defined, 307 equation for, 308, 328 population proportion estimation, 328 Mean advantages and disadvantages, 115 arithmetic, 108 binomial distributions, 210–212 data-level issues in computation of, 113–115 defined, 98 discrete distributions, 199–201 extreme values impacting, 102–103 hypothesis testing for, 341–359 Poisson distributions, 220 population See Population mean sample mean, 101–102, 264, 265–266 sampling distribution of, 272–283 sampling distribution of a proportion, 289 uniform distribution, 252 U-statistic, 720 weighted, 108–109 Wilcoxon, 724 Mean absolute deviation (MAD), 671, 672 Mean paired difference, 411–412 Mean square between (MSB), 465, 466 Mean square error (MSE), 465n, 490, 671, 672 Mean square within (MSW), 465, 465n, 466 Measurement error, 36 Median advantages and disadvantages, 115 data array, 103 Z05_GROE0383_10_GE_IDX.indd 853 defined, 103 extreme values impacting, 105 index point equation, 103 nonparametric tests for population medians, 712–715, 717–725 Microsoft Power BI Desktop software, 749–763 applications for, 749–750 components of, 750–753 dashboards, 752 data sets in, 750–751 measures developed in, 760–762 opening screen of, 753 relationships among data tables in, 755–756 reports created by, 751–752, 762–763 retrieving data in, 753–755 tiles in, 752 variable creation in, 759–760 visualizations in, 750, 756–759 Miller, Thomas, 747 Mode, 105–107, 115 Model building, 601, 661 Model-building process, 600–612 coefficient of determination in, 603–604 computation of regression equation, 603–604 confidence interval estimation for regression coefficients, 610–612 development of multiple regression model, 601–602 diagnosis in, 605, 661, 679–680 forecasting, 661 multicollinearity, 609–610 significance, 605–608 specification in, 600–601, 661, 679 standard deviation in, 608–609 Model diagnosis, 605, 661, 679–680 Model fitting, 679 Model specification, 600–601, 661, 679 Moving average, 683 MSB (mean square between), 465, 466 MSE (mean square error), 465, 490, 671, 672 MSW (mean square within), 465, 465, 466 Multicollinearity, 609–610 Multiple coefficient of determination, 604 Multiple regression analysis, 597–649 aptness of, 642–649 assumptions regarding, 598, 642 coefficient of determination, 604–605 comparison with simple linear regression, 598, 599 confidence interval estimate for slope in, 610–612 correlation coefficient in, 602 dependent variables in, 598, 599 dummy variables in, 614–618 estimated model, 599 Excel worksheet and instructions, 602, 604, 611, 612, 617 hyperplane in, 599–600 hypothesis testing in, 605 independent variables in, 598, 599 model-building process, 600–612 multicollinearity in, 609–610 nonlinear relationships in See Nonlinear relationships polynomial model, 622–625 population model, 598 qualitative independent variables in, 614–618 scatter diagrams in, 603 significance test in, 605–608 standard error of the estimate in, 608 stepwise regression, 635–640 Multiplication rule, 177–180 Multiplicative time-series model, 682–683, 686 Mutually exclusive classes of data, 59 Mutually exclusive events, 156–157, 172–173 N Nominal data, 44 Nonlinear relationships, 621–631 interaction effects, 625–628 modeling, 623–625 overview, 621–622 partial F-test, 629–631 polynomial model, 622–628 scatter diagrams of, 624, 625, 626 Nonlinear trend forecasting, 677–681 Nonlinear trends, defined, 663 Nonparametric statistics, 711–733 Kruskal–Wallis one-way ANOVA, 463, 729–733 Mann–Whitney U-test, 717–722 summary of, 737 Wilcoxon matched-pairs signed rank test, 722–725 Wilcoxon signed rank test, 712–715 Nonresponse bias, 35 Nonstatistical sampling, 38–39 Normal distributions, 237–247 applications of, 241–247 approximate areas under normal curve, 247 bivariate, 555 characteristics of, 237, 238 conversion to standard normal, 238–239 defined, 237 density function, 237, 238 empirical rule, 247 Excel worksheet and instructions, 245, 247 importance of, 237 standard normal See Standard normal distributions Null hypothesis ANOVA, 460, 461 defined, 341 formulating, 341, 343–344 p-values for testing, 353–354 Numerical statistical measures, summary of, 139 O Observed frequency, 524 Observer bias, 36 26/08/17 2:58 PM 854 www.downloadslide.net Index Ogives, 65, 67 One-tailed hypothesis tests defined, 352 population mean, 349–350 population variance, 438, 449–451 One-way ANOVA, 459–473 applications of, 459–460, 463–469 assumptions regarding, 460, 461–463 balanced design, 460, 463 between-sample variation, 461 defined, 459 Excel worksheet and instructions, 468–469, 472 factor in, 459 fixed effects in, 473 Kruskal–Wallis, 463, 729–733 levels in, 460 partitioning sum of squares, 460–461 random effects in, 473 sum of squares between, 464 sum of squares within, 465 table, 463–466 total sum of squares, 461, 463–464 total variation in data, 460, 461 Tukey-Kramer procedure for multiple comparisons, 470–473 within-sample variation, 461 Open-ended classes of data, 60 Open-end questions, 32 Ordinal data, 45 P Paired difference, 411–412 Paired samples, 410–416 confidence interval estimation for, 412, 413–414 defined, 410 estimation using, 411–413 hypothesis testing for, 414–416 point estimate, 411–412 population mean, 412, 413–414 in randomized complete block ANOVA, 477 rationale for using, 411 standard deviation, 412 t-test statistic for, 414–416 Parameters consistent estimator of, 277 defined, 38, 98, 265 single population See Single population parameters unbiased estimator of, 274 Pareto, Alfredo, 89 Pareto charts, 88–89 Pareto principle, 89 Partial F-test, 629–631 Partitioned sum of squares, 460–461, 479, 489 Pascal, Blaise, 153 Pearson product moment correlation, 552 Percentiles, 109–111 Personal interviews, 29, 34 Physical measurement, 35 Pie charts, 77–78 Z05_GROE0383_10_GE_IDX.indd 854 Pilot samples, 321–322 Point estimates, 302, 303, 411–412 Poisson distributions, 217–221 applications of, 197, 218, 219–221 assumptions regarding, 217–218 characteristics of, 217 defined, 217 derivation of, 217 equation for, 218 Excel worksheet and instructions, 221 mean of, 220 standard deviation of, 220 table of, 219 Polynomial regression model, 622–628 complete, 622 composite, 627, 628 curvilinear relationships, 623–625 equation for, 622 Excel worksheet and instructions, 625, 626, 627–628 graphical representations of, 622–623 interaction effects, 625–628 second-order, 622, 625–628 third-order, 622, 623 Pooled estimator, 421 Poorly worded questions, 33 Population, defined, 37–38 Population coefficient of variation, 130 Population mean applications of, 98–100, 101 computing, 100–101 confidence interval estimates for, 302–310, 313, 314–315 defined, 98 equation for, 98, 265 estimating difference between two independent samples, 388–395 Excel worksheet and instructions, 101 hypothesis testing and, 355–359, 398–407 one-tailed hypothesis test about, 349–350 paired samples, 412, 413–414 sample size determination for estimation of, 319–322 Population median, nonparametric tests for, 712–715, 717–725 Population multiple regression model, 598 Population proportion calculating beta for, 373–374 confidence interval estimate for, 326–327, 419 equation for, 286 estimating, 325–330, 419–420 hypothesis testing, 362–365, 420–423 margin of error, 328 sample size for estimating, 328–330 standard error for, 326 Population variances applications for, 123–124 chi-square tests for, 435–440 confidence interval estimation for, 440–441 equations for, 121, 122 F-test for, 445–451 hypothesis for, 436–437 hypothesis testing for, 444–451 one-tailed hypothesis tests for, 438, 449–451 two-tailed hypothesis tests for, 439–440, 447–448 unequal, 394–395, 406–407, 406n Power curve, 375 Power of the test, 374–375, 381 PPI (Producer Price Index), 666, 667 Prediction interval for y, given x, 582 Predictive analytics, 744, 747–748 Prescriptive analytics, 744 Primary clusters, 42 Probability, 152–190 addition rule for, 166–168, 169–173, 180 classical assessment, 158–159 complement rule for, 168–169 conditional, 173–177, 180–183 defined, 153 events of interest, defining, 155–156 experiments, 154 independent and dependent events in, 157–158 joint, 173 marginal, 173 measuring, 165–173 methods of assigning, 158–162 multiplication rule for, 177–180 mutually exclusive events, 156–157 possible values and summation of possible values, 165–166 relative frequency assessment, 159–161 rules of, 165–183 sample space, 154–156 subjective assessment, 161–162 summary of rules and equations, 189–190 terminology associated with, 153–158 tree diagrams, 154–155, 176, 178–179 Probability distributions See Continuous probability distributions; Discrete probability distributions Probability sampling, 39 See also Statistical sampling techniques Producer Price Index (PPI), 666, 667 Proportions pooled estimator for, 421 population See Population proportion sample proportion, 286, 287, 325 sampling distribution of, 286–291 sampling error, 287–288 z-test statistic for, 363, 421 p-value, 351–352, 353–354, 401 Q Qualitative data bar charts for, 62 defined, 43–44 dummy variables, 614–618 frequency distributions for, 56, 57 multiple regression analysis, 614–618 Qualitative forecasting, 661 Quantitative data bivariate relationship between, 86 defined, 43–44 26/08/17 2:58 PM www.downloadslide.net Index 855 frequency distributions for, 56 histograms for, 62 scatter diagrams for, 86 stem and leaf diagrams for, 78 Quantitative forecasting, 661 Quartiles, 111 Questionnaires, 29, 31–34 Questions closed-end, 31 demographic, 31 leading, 33 open-end, 32 poorly worded, 33 R Random component of time-series data, 665 Random effects, 473 Randomized complete block ANOVA, 477–485 applications of, 478–481 assumptions regarding, 478 Excel worksheets and instructions, 480 Fisher’s least significant difference test, 484–485 partitioning sums of squares in, 479 performing, 481–483 sum of squares for blocking in, 479 sum of squares within, 479 table, 480 Type II errors in, 481 Random numbers sampling, 39–40 Random numbers table, 40 Random variables, 197–198 Range, 119–121 Rank data, 45 Ratio data, 45 Ratio sampling, 39 Ratio-to-moving-average method, 682, 684 Recurrence period, 663 Regression analysis applications of, 560, 561–562 assumptions regarding, 560–561 coefficient of determination, 571–574, 604–605 confidence interval estimate in, 579–580, 610–612 decision making, 578–579 defined, 86 descriptive purposes, 578–580 dummy variables in, 614–618 estimated regression model, 562, 564 Excel worksheet and instructions, 579 full regression, 635 hyperplane in, 599–600 least squares criterion, 562, 564, 565 least squares regression properties, 566–568 multicollinearity in, 609–610 multiple See Multiple regression analysis nonlinear relationships in See Nonlinear relationships partial F-test, 629–631 polynomial model, 622–625 prediction, 580–582 Z05_GROE0383_10_GE_IDX.indd 855 problems using, 582–584 residuals, 562, 566 significance tests in, 568–574 simple linear See Simple linear regression standard error of the estimate, 608 stepwise, 635–637 sum of squares error, 562, 563, 566, 571 sum of squares regression, 571–572 test statistic for the slope, 569 total sum of squares, 571 Regression coefficients confidence interval estimation for, 610–612 defined, 561 intercept, 561 slope See Regression slope coefficient t-test for significance of, 607–608 Regression hyperplane, 599–600 Regression line, 561–562, 567 Regression slope coefficient defined, 561 Excel worksheet and instructions, 570 interval estimate for, 579–580 significance of, 568–569, 605, 606 standard error of, 569 Relative frequency, 55–56, 65–67 Relative frequency assessment, 159–161 Relative frequency distributions, 55–56, 61 Replications, two-factor ANOVA with, 488–494 Reports, from Microsoft Power BI Desktop software, 751–752, 762–763 Research hypothesis, 342–343 Residuals analysis of, 643–648 assumptions regarding, 643 corrective actions for, 648–649 defined, 562, 643 equal variances, 645 equation for, 643 Excel worksheets and instructions, 671 forecasting errors, 670–671 histograms of, 648 independence of, 646–647 linearity of, 643–644 normality, 647–648 scatter diagrams of, 643–647 standardized, 647–648 sum of, 566 sum of squared residuals, 566 Review Sections chapters 1–3, 146–151 chapters 8–12, 505–520 Right-skewed data, 104, 105 R software, 747 r * c contingency tables, 539–541 S Sample coefficient of variation, 130 Sample mean, 101–102, 264, 265–266 Sample proportion, 286, 287, 325 Samples defined, 37–38 independent, 388 paired See Paired samples pilot, 321–322 split, 676–677 Sample size calculation of, 320–322 confidence interval, 310, 315, 320–322 equation for, 320, 328 estimates of, 319–322, 328–330 goodness-of-fit tests, 524 pilot sample, 321–322 population mean, 319–322 population proportion, 328–330 role in sampling error, 267–269 Sample space, 154–156 Sample standard deviation, 124–126 Sample variance, 124–126, 446 Sampling distribution of a proportion, 286–291 applications of, 286–287, 289–291 mean, 289 sampling error, 287–288 standard error, 289 Theorem 5, 289 Sampling distribution of the mean, 272–283 applications of, 273–275 Central Limit Theorem, 279–283, 288 consistent estimator, 277 defined, 273 Excel worksheet and instructions, 273 from normal populations, 275–279 overview, 272–273 Theorem 1, 274 Theorem 2, 274 Theorem 3, 275 unbiased estimator, 274 Sampling error, 264–269 applications of, 265–266 calculating, 264, 266–267 defined, 264, 302 role of sample size in, 267–269 single-proportion, 287–288 Sampling techniques, 38–42 cluster, 41–42 nonstatistical, 38–39 random numbers, 39–40 simple random, 39, 265 statistical, 38, 39–42 stratified random, 40–41 summary of, 50 systematic random, 41 Sampling without replacement, 39 Sampling with replacement, 39 Scatter diagrams/scatter plots constructing, 86–88 defined, 86, 551 dependent variables, 86, 551 Excel worksheet and instructions, 87, 553, 568 independent variables, 86, 551 nonlinear relationships, 624, 625, 626 residuals, 643–647 two-variable relationships in, 551, 552, 603 26/08/17 2:58 PM 856 www.downloadslide.net Index Seasonal component of time-series data, 663–664, 682–687 Seasonal indexes computing, 682–685 defined, 682 deseasonalization of, 686–687 Excel worksheet and instructions, 683–685 multiplicative model, 682–683, 686 normalization of, 686 Second-order regression model, 622, 625–628 Selection bias, 35–36 Serial correlation, 673 Significance level, 345–346 Significance tests coefficient of determination, 573 correlation, 553–555 multiple regression analysis, 605–608 regression analysis, 568–574 Simple index number, 665–666 Simple linear regression, 560–574 assumptions regarding, 560–561 coefficient of determination, 571–574 comparison with multiple regression, 598, 599 correlation, 567–568 defined, 560 equation for, 560 Excel worksheet and instructions, 565–566, 567, 568, 570, 624 least squares criterion, 562, 564, 565 least squares regression properties, 566–568 residuals, 562, 566 significance tests in, 568–574 sum of squares error, 562, 563, 566, 571 sum of squares regression, 571–572 test statistic for, 569 total sum of squares, 571 Simple random sampling, 39, 265 Simple regression analysis, 560 Single exponential smoothing, 691–694 Single population parameters, 301–330 confidence interval estimate for population mean, 303–310 determining sample size for estimating population mean, 319–322 estimating population proportion, 325–330 point estimates and confidence intervals, 302–303 Student’s t-distributions, 310–315 Single-proportion sampling error, 287–288 Skewed data, 104–105 Skewness statistic, 105 Slope coefficient See Regression slope coefficient Smoothing methods, 691–698 Split samples, 676–677 SSBL (sum of squares blocking), 479 SSE See Sum of squares error SSR (sum of squares regression), 571–572 SST (total sum of squares), 461, 463–464, 479, 489, 571 SSW (sum of squares within), 465, 469 Standard deviation Z05_GROE0383_10_GE_IDX.indd 856 binomial distributions, 212–213 computing, 123–124, 125–126 defined, 121 discrete distributions, 199–201 paired differences, 412 Poisson distributions, 220 population standard deviation, 122 population variance, 121–124 regression model, 608–609 sample standard deviation, 124–126 sample variance, 124–126 uniform distribution, 252 U-statistic, 720 Wilcoxon, 724 Standard error defined, 303 difference between two means, 388 equations for, 289, 326, 608 the estimate, 569, 608 population proportion, 326 regression slope, 569, 570 sampling distribution of a proportion, 289 Standardized data values, 133–135 Standardized residuals, 647–648 Standard normal distributions, 238–247 applications of, 239–240, 241–247 conversion of normal distributions to, 238–239 table, 240–241, 242 Standard stepwise regression, 637–638 States of nature, 344 Statistical errors, 344–345 Statistical estimations, 301 Statistical hypothesis testing, 341 Statistical inferential procedures, 26, 28, 340 Statistical sampling techniques advantages of, 38 cluster, 41–42 random numbers, 39–40 simple random, 39, 265 stratified random, 40–41 systematic random, 41 Statistics, defined, 38, 98 Stem and leaf diagrams, 78–80 Stepwise regression, 635–640 backward elimination procedure, 635 best subsets method, 638–640 forward selection procedure, 635–637 standard, 637–638 Strata, 40 Stratified random sampling, 40–41 Structured interviews, 34 Student’s t-distribution See t-distribution Subjective probability assessment, 161–162 Substrata, 41 Sum of squares between, 464 Sum of squares blocking (SSBL), 479 Sum of squares error (SSE) defined, 479 equation for, 566, 571 Excel worksheet and instructions, 630–631 interaction, 629–630 regression analysis, 562, 563 Sum of squares partitioning, 460–461, 479, 489 Sum of squares regression (SSR), 571–572 Sum of squares within (SSW), 465, 469 Surveys telephone, 29, 30–31 written, 29, 31–34 Symmetric data, 104–105 Symmetric distributions, 237 Systematic random sampling, 41 T Tables binomial distributions, 209–210 discrete data, 53, 54 frequency distributions, 53, 54–55 joint relative frequency, 69, 70 one-way ANOVA, 463–466 Poisson distributions, 219 randomized complete block ANOVA, 480 random numbers, 40 r * c contingency, 539–541 standard normal distributions, 240–241, 242 t-distribution, 311, 312 * contingency, 535–539 two-factor ANOVA, 490 Tchebysheff’s Theorem, 133 t-distribution applications for, 313–314 assumptions regarding, 311, 401 degrees of freedom, 310–311 designed, 310 equation for, 311 estimating difference between two population means, 390–393 table, 311, 312 unequal variances, 394–395, 406 Telephone surveys, 29, 30–31 Test statistics chi-square contingency, 536 chi-square goodness-of-fit, 524, 525 correlation coefficient, 554–555 defined, 348 F-test, 444, 629–630 H-statistic, 731, 733 significance of coefficient of determination, 573 simple linear regression, 569 t-test See t-test statistic U-statistics, 719, 720 Wilcoxon, 714, 725 z-test See z-test statistic Third-order regression model, 622, 623 Tiles, in Microsoft Power BI Desktop software, 752 Time-series data components of, 662–665 cyclical component of, 664 defined, 44 deflating, 666–667 deseasonalization of, 686 index numbers, 665–667 line charts for, 83 31/08/17 5:21 PM www.downloadslide.net Index 857 Producer Price Index, 666, 667 random component of, 665 seasonal component of, 663–664, 682–687 Total sum of squares (SST), 461, 463–464, 479, 489, 571 Total variation in data, 460, 461 Tree diagrams, 154–155, 176, 178–179 Trend-based forecasting, 668–687 autocorrelation, 672–676 cautionary guidelines, 680–681 comparison of forecast values to actual data, 670–677 development of model for, 668–670 linear, 668–670 mean absolute deviation in, 671, 672 mean squared error in, 671, 672 nonlinear, 677–681 seasonal adjustments in, 681–687 true forecasts and split samples, 676–677 Trend (line) charts, 83–85 Trend projection, 676 Trends, defined, 663 True forecasts, 676–677 t-test statistic assumptions regarding, 355 correlation coefficient, 554–555 equations for, 355, 381, 401, 414, 554, 607 hypothesis testing, 355–359, 401–403, 405–407 paired samples, 414–416 regression coefficient significance, 607–608 Tukey-Kramer procedure for multiple comparisons, 470–473 Two-factor ANOVA, 488–494 applications for, 488–490 assumptions regarding, 490 defined, 488 equations for, 491 Excel worksheet and instructions, 492–494 Z05_GROE0383_10_GE_IDX.indd 857 interaction in, 490–494 partitioning sum of squares in, 489 table, 490 Two-tailed hypothesis tests applications of, 354–355 calculating beta for, 372–373 defined, 352 population variance, 439–440, 447–448 p-value for, 353–354 Type I errors, 344–345, 368 Type II errors calculating beta, 346, 368–374 defined, 344 examples of, 345 randomized complete block ANOVA, 481 shortcut equations, 122, 124 unequal, 394–395, 406–407, 406 Variation, 119–126 between-sample, 461 coefficient of, 130–131 defined, 119 Excel worksheet and instructions, 126 population variance and standard deviation, 121–124 range as measure of, 119–121 sample variance and standard deviation, 124–126 within-sample, 461 Venn diagrams, 170, 173 Visualizations of data, 750, 756–759 U W Unbiased estimator, 274 Uniform probability distributions, 250–252 Unimodal distributions, 237 Unstructured interviews, 34 U-statistics, 719, 720 V Validity, 36 Variables continuous, 57, 61–62 creation in Microsoft Power BI Desktop software, 759–760 dependent, 86, 551, 598 dummy, 614–616 independent, 86, 551, 598, 614 random, 197–198 Variance inflation factor, 610, 611 Variances computing, 123–124 defined, 121 population See Population variances residuals, 645 sample variance, 124–126, 446 Weighted mean, 108–109 Wilcoxon matched-pairs signed rank test, 722–725 Wilcoxon signed rank test, 712–715 Within-sample variation, 461 Written questionnaires, 29, 31–34 Z z-test statistic defined, 348 difference between population proportions, 421 equations for, 348, 363, 381, 399, 421 hypothesis testing, 348–351, 399–400 independent samples, 399 proportions, 363, 421 z-values adjusted for finite population correction factor, 278 sampling distribution of mean, 278 sampling distribution of proportion, 290 standardized, 239 standard normal table, 240, 242 26/08/17 2:58 PM www.downloadslide.net This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM www.downloadslide.net Credits Photographs FRONTM AT T E R p. 7, David F Groebner; p. 7, Patrick W Shannon; p. 7, Phillip C Fry CHAPTE R p. 25, Adike/Shutterstock; p. 38, Besjunior/Fotolia; p. 40, Fstockfoto/ Shutterstock; p. 45, Joe 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OR STATUTORY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT IN NO EVENT SHALL MICROSOFT AND/OR ITS RESPECTIVE SUPPLIERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF INFORMATION AVAILABLE FROM THE SERVICES THE DOCUMENTS AND RELATED GRAPHICS CONTAINED HEREIN COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN MICROSOFT AND/ OR ITS RESPECTIVE SUPPLIERS MAY MAKE IMPROVEMENTS AND/OR CHANGES IN THE PRODUCT(S) AND/OR THE PROGRAM(S) DESCRIBED HEREIN AT ANY TIME PARTIAL SCREEN SHOTS MAY BE VIEWED IN FULL WITHIN THE SOFTWARE VERSION SPECIFIED MICROSOFT® WINDOWS®, and MICROSOFT OFFICE® ARE REGISTERED TRADEMARKS OF THE MICROSOFT CORPORATION IN THE U.S.A AND OTHER COUNTRIES THIS BOOK IS NOT SPONSORED OR ENDORSED BY OR AFFILIATED WITH THE MICROSOFT CORPORATION 31/08/17 5:36 PM www.downloadslide.net Values of t for Selected Probabilities df = 10 0.05 0.05 t = –1.8125 t = 1.8125 t Probabilites (Or Areas Under t-Distribution Curve) Conf Level One Tail Two Tails 0.1 0.45 0.9 0.3 0.35 0.7 0.5 0.25 0.5 0.7 0.15 0.3 d f Z07_GROE0383_10_GE_BEP.indd 861 0.8 0.1 0.2 0.9 0.05 0.1 0.95 0.025 0.05 0.98 0.01 0.02 0.99 0.005 0.01 Values of t 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 40 50 60 70 80 90 100 250 500 0.1584 0.1421 0.1366 0.1338 0.1322 0.1311 0.1303 0.1297 0.1293 0.1289 0.1286 0.1283 0.1281 0.1280 0.1278 0.1277 0.1276 0.1274 0.1274 0.1273 0.1272 0.1271 0.1271 0.1270 0.1269 0.1269 0.1268 0.1268 0.1268 0.1267 0.1265 0.1263 0.1262 0.1261 0.1261 0.1260 0.1260 0.1258 0.1257 0.5095 0.4447 0.4242 0.4142 0.4082 0.4043 0.4015 0.3995 0.3979 0.3966 0.3956 0.3947 0.3940 0.3933 0.3928 0.3923 0.3919 0.3915 0.3912 0.3909 0.3906 0.3904 0.3902 0.3900 0.3898 0.3896 0.3894 0.3893 0.3892 0.3890 0.3881 0.3875 0.3872 0.3869 0.3867 0.3866 0.3864 0.3858 0.3855 1.0000 0.8165 0.7649 0.7407 0.7267 0.7176 0.7111 0.7064 0.7027 0.6998 0.6974 0.6955 0.6938 0.6924 0.6912 0.6901 0.6892 0.6884 0.6876 0.6870 0.6864 0.6858 0.6853 0.6848 0.6844 0.6840 0.6837 0.6834 0.6830 0.6828 0.6807 0.6794 0.6786 0.6780 0.6776 0.6772 0.6770 0.6755 0.6750 1.9626 1.3862 1.2498 1.1896 1.1558 1.1342 1.1192 1.1081 1.0997 1.0931 1.0877 1.0832 1.0795 1.0763 1.0735 1.0711 1.0690 1.0672 1.0655 1.0640 1.0627 1.0614 1.0603 1.0593 1.0584 1.0575 1.0567 1.0560 1.0553 1.0547 1.0500 1.0473 1.0455 1.0442 1.0432 1.0424 1.0418 1.0386 1.0375 3.0777 1.8856 1.6377 1.5332 1.4759 1.4398 1.4149 1.3968 1.3830 1.3722 1.3634 1.3562 1.3502 1.3450 1.3406 1.3368 1.3334 1.3304 1.3277 1.3253 1.3232 1.3212 1.3195 1.3178 1.3163 1.3150 1.3137 1.3125 1.3114 1.3104 1.3031 1.2987 1.2958 1.2938 1.2922 1.2910 1.2901 1.2849 1.2832 6.3138 2.9200 2.3534 2.1318 2.0150 1.9432 1.8946 1.8595 1.8331 1.8125 1.7959 1.7823 1.7709 1.7613 1.7531 1.7459 1.7396 1.7341 1.7291 1.7247 1.7207 1.7171 1.7139 1.7109 1.7081 1.7056 1.7033 1.7011 1.6991 1.6973 1.6839 1.6759 1.6706 1.6669 1.6641 1.6620 1.6602 1.6510 1.6479 12.7062 4.3027 3.1824 2.7764 2.5706 2.4469 2.3646 2.3060 2.2622 2.2281 2.2010 2.1788 2.1604 2.1448 2.1314 2.1199 2.1098 2.1009 2.0930 2.0860 2.0796 2.0739 2.0687 2.0639 2.0595 2.0555 2.0518 2.0484 2.0452 2.0423 2.0211 2.0086 2.0003 1.9944 1.9901 1.9867 1.9840 1.9695 1.9647 31.8205 6.9646 4.5407 3.7469 3.3649 3.1427 2.9980 2.8965 2.8214 2.7638 2.7181 2.6810 2.6503 2.6245 2.6025 2.5835 2.5669 2.5524 2.5395 2.5280 2.5176 2.5083 2.4999 2.4922 2.4851 2.4786 2.4727 2.4671 2.4620 2.4573 2.4233 2.4033 2.3901 2.3808 2.3739 2.3685 2.3642 2.3414 2.3338 63.6567 9.9248 5.8409 4.6041 4.0321 3.7074 3.4995 3.3554 3.2498 3.1693 3.1058 3.0545 3.0123 2.9768 2.9467 2.9208 2.8982 2.8784 2.8609 2.8453 2.8314 2.8188 2.8073 2.7969 2.7874 2.7787 2.7707 2.7633 2.7564 2.7500 2.7045 2.6778 2.6603 2.6479 2.6387 2.6316 2.6259 2.5956 2.5857 ` 0.1257 0.3853 0.6745 1.0364 1.2816 1.6449 1.9600 2.3263 2.5758 14/09/17 1:01 PM www.downloadslide.net Standard Normal Distribution Table 0.3944 z z = 1.25 z 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 0.0000 0.0398 0.0793 0.1179 0.1554 0.1915 0.2257 0.2580 0.2881 0.3159 0.3413 0.3643 0.3849 0.4032 0.4192 0.4332 0.4452 0.4554 0.4641 0.4713 0.4772 0.4821 0.4861 0.4893 0.4918 0.4938 0.4953 0.4965 0.4974 0.4981 0.4987 0.0040 0.0438 0.0832 0.1217 0.1591 0.1950 0.2291 0.2611 0.2910 0.3186 0.3438 0.3665 0.3869 0.4049 0.4207 0.4345 0.4463 0.4564 0.4649 0.4719 0.4778 0.4826 0.4864 0.4896 0.4920 0.4940 0.4955 0.4966 0.4975 0.4982 0.4987 0.0080 0.0478 0.0871 0.1255 0.1628 0.1985 0.2324 0.2642 0.2939 0.3212 0.3461 0.3686 0.3888 0.4066 0.4222 0.4357 0.4474 0.4573 0.4656 0.4726 0.4783 0.4830 0.4868 0.4898 0.4922 0.4941 0.4956 0.4967 0.4976 0.4982 0.4987 0.0120 0.0517 0.0910 0.1293 0.1664 0.2019 0.2357 0.2673 0.2967 0.3238 0.3485 0.3708 0.3907 0.4082 0.4236 0.4370 0.4484 0.4582 0.4664 0.4732 0.4788 0.4834 0.4871 0.4901 0.4925 0.4943 0.4957 0.4968 0.4977 0.4983 0.4988 0.0160 0.0557 0.0948 0.1331 0.1700 0.2054 0.2389 0.2704 0.2995 0.3264 0.3508 0.3729 0.3925 0.4099 0.4251 0.4382 0.4495 0.4591 0.4671 0.4738 0.4793 0.4838 0.4875 0.4904 0.4927 0.4945 0.4959 0.4969 0.4977 0.4984 0.4988 0.0199 0.0596 0.0987 0.1368 0.1736 0.2088 0.2422 0.2734 0.3023 0.3289 0.3531 0.3749 0.3944 0.4115 0.4265 0.4394 0.4505 0.4599 0.4678 0.4744 0.4798 0.4842 0.4878 0.4906 0.4929 0.4946 0.4960 0.4970 0.4978 0.4984 0.4989 0.0239 0.0636 0.1026 0.1406 0.1772 0.2123 0.2454 0.2764 0.3051 0.3315 0.3554 0.3770 0.3962 0.4131 0.4279 0.4406 0.4515 0.4608 0.4686 0.4750 0.4803 0.4846 0.4881 0.4909 0.4931 0.4948 0.4961 0.4971 0.4979 0.4985 0.4989 0.0279 0.0675 0.1064 0.1443 0.1808 0.2157 0.2486 0.2794 0.3078 0.3340 0.3577 0.3790 0.3980 0.4147 0.4292 0.4418 0.4525 0.4616 0.4693 0.4756 0.4808 0.4850 0.4884 0.4911 0.4932 0.4949 0.4962 0.4972 0.4979 0.4985 0.4989 0.0319 0.0714 0.1103 0.1480 0.1844 0.2190 0.2517 0.2823 0.3106 0.3365 0.3599 0.3810 0.3997 0.4162 0.4306 0.4429 0.4535 0.4625 0.4699 0.4761 0.4812 0.4854 0.4887 0.4913 0.4934 0.4951 0.4963 0.4973 0.4980 0.4986 0.4990 0.0359 0.0753 0.1141 0.1517 0.1879 0.2224 0.2549 0.2852 0.3133 0.3389 0.3621 0.3830 0.4015 0.4177 0.4319 0.4441 0.4545 0.4633 0.4706 0.4767 0.4817 0.4857 0.4890 0.4916 0.4936 0.4952 0.4964 0.4974 0.4981 0.4986 0.4990 Z07_GROE0383_10_GE_BEP.indd 862 14/09/17 1:01 PM www.downloadslide.net This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM www.downloadslide.net This page intentionally left blank 561590_MILL_MICRO_FM_ppi-xxvi.indd 24/11/14 5:26 PM www.downloadslide.net ... 1.4 Data Types and Data Measurement Levels  43 Quantitative and Qualitative Data 43 Time-Series Data and Cross-Sectional Data 44 Data Measurement Levels 44 1.5 A Brief Introduction to Data Mining ... Pie Charts, and Stem and Leaf Diagrams  74 Bar Charts 74 Pie Charts 77 Stem and Leaf Diagrams 78 2.3 Line Charts, Scatter Diagrams, and Pareto Charts  83 Line Charts 83 Scatter Diagrams 86 Pareto... their decision making Because of the recent advances in software and database systems, managers are able to analyze data in more depth than ever before Disciplines called business analytics/business