Statistics for manager using microsoft excel 7th global edtion by levine

Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine Statistics for manager using microsoft excel 7th global edtion by levine

– New! Introductory chapter “Let’s Get Started: Big Things to Learn First” defines business analytics and big data and explains how they are changing the face of statistics – New! Continuing end-of-chapter cases help students to apply theory into practice SEVENTH EDITION The seventh edition of Statistics for Managers Using Microsoft® Excel focuses on making statistics even more relevant to the business world today Students are encouraged to see the relevance of statistics in their own careers by providing examples drawn from the areas in which they may be specializing Using Microsoft ® Excel – Updated! Microsoft Windows and OS X Excel-Based Solutions guides are comprehensive and easy to use Statistics for Managers This Global Edition has been edited to include enhancements making it more relevant to students outside the United States The editorial team at Pearson has worked closely with educators around the globe to include: Levine Stephan Szabat This is a special edition of an established title widely used by colleges and universities throughout the world Pearson published this exclusive edition for the benefit of students outside the United States and Canada If you purchased this book within the United States or Canada you should be aware that it has been imported without the approval of the Publisher or Author Pearson International Edition GLOBAL EDITION GLOBAL EDITION GLOBAL EDITION Statistics for Managers Using Microsoft ® Excel SEVENTH EDITION David M Levine • David F Stephan • Kathryn A Szabat A ROADMAP FOR SELECTING A STATISTICAL METHOD Data Analysis Task Describing a group or several groups For Numerical Variables Ordered array, stem-and-leaf display, frequency distribution, relative frequency distribution, percentage distribution, cumulative percentage distribution, histogram, polygon, cumulative percentage polygon (Sections 2.2 2.4) For Categorical Variables Summary table, bar chart, pie chart, Pareto chart (Sections 2.1, 2.3) Mean, median, mode, geometric mean, quartiles, range, interquartile range, standard deviation, variance, coefficient of variation, skewness, kurtosis, boxplot, normal probability plot (Sections 3.1, 3.2, 3.3, 6.3) Index numbers (bonus eBook Section 16.8) Inference about one group Confidence interval estimate of the mean (Sections 8.1 and 8.2) t test for the mean (Section 9.2) Chi-square test for a variance or standard deviation (bonus eBook Section 12.7) Confidence interval estimate of the proportion (Section 8.3) Z test for the proportion (Section 9.4) Comparing two groups Tests for the difference in the means of two independent populations (Section 10.1) Wilcoxon rank sum test (Section 12.5) Paired t test (Section 10.2) Z test for the difference between two proportions (Section 10.3) Chi-square test for the difference between two proportions (Section 12.1) F test for the difference between two variances (Section 10.4) McNemar test for two related samples (bonus eBook Section 12.6) Comparing more than two groups One-way analysis of variance for comparing several means (Section 11.1) Kruskal-Wallis test (Section 12.6) Two-way analysis of variance (Section 11.2) Randomized block design (bonus eBook Section 11.3) Chi-square test for differences among more than two proportions (Section 12.2) Analyzing the relationship between two variables Scatter plot, time series plot (Section 2.5) Covariance, coefficient of correlation (Section 3.5) Simple linear regression (Chapter 13) t test of correlation (Section 13.7) Time series forecasting (Chapter 16) Contingency table, side-by-side bar chart, PivotTables (Sections 2.1, 2.3, 2.8) Chi-square test of independence (Section 12.3) Analyzing the relationship between two or more variables Multiple regression (Chapters 14 and 15) Multidimensional contingency tables (Section 2.7) PivotTables and business analytics (Section 2.8) Logistic regression (Section 14.7) Predictive analytics and data mining (Section 15.6) Statistics for Managers Using Microsoft Excel SevenTH ediTion Global edition Statistics for Managers Using Microsoft Excel SevenTH ediTion Global edition david M Levine Department of Statistics and Computer Information Systems Zicklin School of Business, Baruch College, City University of New York david F Stephan Two Bridges Instructional Technology Kathryn A Szabat Department of Business Systems and Analytics School of Business, La Salle University Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City S~ ao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editor in Chief: Donna Battista Senior Acquisitions Editor, International: Steven Jackson Programme Editor, International: Leandra Paoli Editorial Project Manager: Mary Kate Murray Editorial Assistant: Ashlee Bradbury Director of Marketing: Maggie Moylan Marketing Manager: Jami Minard Marketing Manager, International: Dean Erasmus Senior Managing Editor: Judy Leale Production Project Manager: Jane Bonnell Senior Manufacturing Controller, Production, International: Trudy Kimber Creative Director: Blair Brown Art Director: Steve Frim Interior Designers: Dina Curro/Suzanne Behnke Cover Designer: Jodi Notowitz Cover Image: Serp/Shutterstock Associate Media Project Manager, Editorial: Sarah Peterson Media Producer: Christina Maestri Media Project Manager, Production: John Cassar Full-Service Project Management: PreMediaGlobal Pearson Education Limited Edinburgh Gate Harlow Essex CM20 2JE England and Associated Companies throughout the world Visit us on the World Wide Web at: www.pearson.com/uk © Pearson Education Limited 2014 The rights of David M Levine, David F Stephan and Kathryn A Szabat to be identified as authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 Authorised adaptation from the United States edition, entitled Statistics for Managers: Using Microsoft Excel, 7th Edition, ISBN: 978-0-13-306181-9 by David M Levine, David F Stephan and Kathryn A Szabat, published by Pearson Education, Inc., © 2014 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 licence 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 Microsoft and/or its respective suppliers make no representations about the suitability of the information contained in the documents and related graphics published as part of the services for any purpose All such documents and related graphics are provided “as is” without warranty of any kind Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied 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® and Windows® 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 Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text ISBN 13: 978-0-273-78711-2 ISBN 10: 0-273-78711-X British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library 10 17 16 15 14 13 Typeset in TimesNewRomanPS by PreMediaGlobal, Inc Printed and bound by Courier/Kendallville in The United States of America The publisher’s policy is to use paper manufactured from sustainable forests To our spouses and children, Marilyn, Mary, Sharyn, and Mark, and to our parents, in loving memory, Lee, Reuben, Ruth, Francis, and William, in honor, Mary About the Authors The authors of this book: Kathryn Szabat, David Levine, and David Stephan at a Decision Sciences Institute meeting David M Levine is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York) He received B.B.A and M.B.A degrees in statistics from City College of New York and a Ph.D from New York University in industrial engineering and operations research He is nationally recognized as a leading innovator in statistics education and is the co-author of 44 books, including such bestselling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, currently in its second edition, Six Sigma for Green Belts and Champions and Design for Six Sigma for Green Belts and Champions, and the author of Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and Quality Management, third edition, McGraw-Hill/Irwin He is also the author of Video Review of Statistics and Video Review of Probability, both published by Video Aided Instruction, and the statistics module of the MBA primer published by Cengage Learning He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Decision Sciences Journal of Innovative Education, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and he has given numerous talks at the Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences Levine has also received several awards for outstanding teaching and curriculum development from Baruch College David F Stephan is an independent instructional technologist He was an Instructor/Lecturer of Computer Information Systems at Baruch College (City University of New York) for over 50 years and also served as an Assistant to the Provost and to the Dean of the School of Business & Public Administration for computing He pioneered the use of computer classrooms for business teaching, devised interdisciplinary multimedia tools, and created techniques for teaching computer applications in a business context He also conducted the first large-scale controlled experiment to show the benefit of teaching Microsoft Excel in a business case context to undergraduate students ABoUT THE AUTHoRS An avid developer, he created multimedia courseware while serving as the Assistant Director of a Fund for the Improvement of Postsecondary Education (FIPSE) project at Baruch College Stephan is also the originator of PHStat, the Pearson Education statistical add-in for Microsoft Excel and a co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics and Practical Statistics by Example Using Microsoft Excel and Minitab He is currently developing ways to extend the instructional materials that he and his co-authors develop to mobile and cloud computing platforms as well as develop social-media facilitated means to support learning in introductory business statistics courses Stephan received a B.A in geology from Franklin and Marshall College and a M.S in computer methodology from Baruch College (City University of New York) Kathryn A Szabat is Associate Professor and Chair of Business Systems and Analytics at LaSalle University She teaches undergraduate and graduate courses in business statistics and operations management She also teaches as Visiting Professor at the Ecole Superieure de Commerce et de Management (ESCEM) in France Szabat’s research has been published in International Journal of Applied Decision Sciences, Accounting Education, Journal of Applied Business and Economics, Journal of Healthcare Management, and Journal of Management Studies Scholarly chapters have appeared in Managing Adaptability, Intervention, and People in Enterprise Information Systems; Managing, Trade, Economies and International Business; Encyclopedia of Statistics in Behavioral Science; and Statistical Methods in Longitudinal Research Szabat has provided statistical advice to numerous business, non-business, and academic communities Her more recent involvement has been in the areas of education, medicine, and nonprofit capacity building Szabat received a B.S in mathematics from State University of New York at Albany and M.S and Ph.D degrees in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania www.downloadslide.net 778 Self-Test Solutions and Answers to Selected Even-Numbered Problems 18.48 (a) p = 0.2702, LCL = 0.1700, UCL = 0.3703 (b) Yes, RudyBird’s market share is in control before the in-store promotion (c) All seven days of the in-store promotion are above the UCL The promotion increased market share 18.50 (a) p = 0.75175, LCL = 0.62215, UCL = 0.88135 Although none of the points are outside the control limits, there is a clear pattern over time, with the last 13 points above the center line Therefore, this process is not in control (b) Because the increasing trend begins around Day 20, this change in method would be the assignable cause (c) The control chart would have been developed using the first 20 days, and then a different control chart would be used for the final 20 points because they represent a different process 18.52 (a) p = 0.1198, LCL = 0.0205, UCL = 0.2191 (b) Day 24 is below the LCL; therefore, the process is out of control (c) Special causes of variation should be investigated to improve the process Next, the process should be improved to decrease the proportion of undesirable trades 18.54 Separate p charts should be developed for each food for each shift: Kidney—Shift 1: p = 0.01395, UCL = 0.02678, LCL = 0.00112 Although there are no points outside the control limits, there is a strong increasing trend in nonconformances over time Kidney—Shift 2: p = 0.01829, UCL = 0.03329, LCL = 0.00329 Although there are no points outside the control limits, there is a strong increasing trend in nonconformances over time Shrimp—Shift 1: p = 0.006995, UCL = 0.01569, LCL = There are no points outside the control limits, and there is no pattern over time Shrimp—Shift 2: p = 0.01023, UCL = 0.021, LCL = There are no points outside the control limits, and there is no pattern over time The team needs to determine the reasons for the increase in nonconformances for the kidney product The production volume for kidney is clearly decreasing for both shifts This can be observed from a plot of the production volume over time The team needs to investigate the reasons for this www.downloadslide.net Index A α (level of significance), 339 A priori probability, 186 Addition rule, 191 Adjusted r2, 562, 610 Algebra, rules for 696 Alternative hypothesis, 336 Among-group variation, 420–421 Analysis of means (ANOM), 428 Analysis of proportions (ANOP), 472 Analysis of variance (ANOVA), 420 Kruskal-Wallis rank test for differences in c medians, 484–487 assumptions of, 484 one-way, 420 assumptions, 428–429 F test for differences in more than two means, 422 F test statistic, 422 Levene’s test for homogeneity of variance, 429–430 summary table, 423 Tukey-Kramer procedure, 426–428 two-way, 433 cell means plot, 441 factorial design, 433 interpreting interaction effects, 441–442 multiple comparisons, 440–441 summary table, 438 testing for factor and interaction effects, 436–437 Analysis ToolPak checking for presence, 721 descriptive statistics, 180 exponential smoothing, 680–681 frequency distribution, 125–126 F test for ratio of two variances, 416 histogram, 130 multiple regression, 598–600 one-way ANOVA, 455 paired t test, 415 pooled-variance t test, 412–413 random sampling, 67 sampling distributions, 297 separate-variance t test, 414 simple linear regression, 551 two-way ANOVA, 457 Analyze, 34 ANOM procedure, 428 ANOP procedure, 472 ANOVA See Analysis of variance (ANOVA) Area of opportunity, 232 Arithmetic mean See Mean Arithmetic operations, rules for, 696 Assumptions analysis of variance (ANOVA), 428–429 of the confidence interval estimate for the mean (σ unknown), 307 of the confidence interval estimate for the proportion, 315 of the F test for the ratio of two variances, 401–402 of Kruskal-Wallis test, 484 of the paired t test, 386 of regression, 518 for  table, 465 for  c table, 470 for r  c table, 473–477 for the t distribution, 348 in testing for the difference between two means, 377 t test for the mean (σ unknown), 350–351 of the Wilcoxon rank sum test, 479 of the Z test for a proportion, 359 Autocorrelation, 522 Autoregressive modeling, 657 steps involved in, on annual time-series data, 662 for trend fitting and forecasting, 657–663 B Bar chart, 85–86 Bayes’ theorem, 202 Best-subsets approach in model building, 619–622 Bias nonresponse, 58 selection, 58 Big data, 37 Binomial distribution, 225 mean of, 230 properties of, 225 shape of, 229 standard deviation of, 230 Binomial probabilities calculating, 227 β Risk, 339 Boxplots, 158–159 Brynne packaging, 549 Business analytics, 34, 36–37 statistical methods in, 626–627 C CardioGood Fitness, 63–64, 121, 179, 211, 275, 331, 410, 453, 495 Categorical data chi-square test for the difference between two proportions, 460–465 chi-square test for c proportions, 467–470 chi-square test of independence, 473–477 organizing, 71–72 visualizing, 85–90 Z test for the difference between two proportions, 393–395 Categorical variables, 48 Causal forecasting methods, 640 Cell means plot, 441 Cells, 40 Central limit theorem, 286 Central tendency, 136–141 Certain event, 186 Chartjunk, 104–105 779 www.downloadslide.net 780 Index Charts bar, 85–86 Pareto, 87–89 pie, 86-87 side-by-side bar, 89 Chebyshev Rule, 164 Chi-square automatic interaction detector (CHAID), 626–627 Chi-square (χ2) distribution, 462 Chi-square (χ2) table, 728 Chi-square (χ2) test for differences between c proportions, 467–470 between two proportions, 460–465 Chi-square (χ2) test of independence, 473–477 Chi-square (χ2) test for a variance or standard deviation, 489 Class boundaries, 77 Class interval width, 76 Class intervals, 76 Class midpoint, 77 Classes, 76 Classification and regression trees (CART), 626–628 Clear Mountain State Surveys, 64, 121, 179, 211, 275, 331, 411, 453, 495–496 Cluster analysis, 627 Cluster sample, 56 Coefficient of correlation, 167–170 inferences about, 529–530 Coefficient of determination, 514–515 Coefficient of multiple determination, 561–562, 609–610 Coefficient of partial determination, 574 Coefficient of variation, 146 Collect, 34 Collectively exhaustive, 54 events, 190 Collinearity of explanatory variables, 615–616 Combinations, 226 Complement, 187 Completely randomized design See also One-way analysis of variance Conditional probability, 194–196 Confidence coefficient, 339 Confidence interval estimation, 300 connection between hypothesis testing and, 346 for the difference between the means of two independent groups, 379 for the difference between the proportions of two independent groups, 397 ethical issues and, 323 for the mean difference, 391 for the mean (σ known), 300–305 for the mean (σ unknown), 306–312 for the mean response, 534 for the proportion, 314–316 of the slope, 529, 567–568 Contingency tables, 72–73, 188, 460 Continuous probability distributions, 257 Continuous variables, 48 Control chart factors, tables, 737 Convenience sampling, 54 Correlation coefficient See Coefficient of correlation Covariance, 166 of a probability distribution, 219–220 Coverage error, 58 Craybill Instrumentation Company case, 634 Critical range, 426 Critical value approach, 342 Critical values, 338 of test statistic, 337–338 Cross-product term, 578 Cross validation, 624 Cumulative percentage distribution, 81–82 Cumulative percentage polygons, 96–97 Cumulative standardized normal distribution, 253 tables, 724–725 Cyclical effect, 641 D Dashboards, 625 Data, 35 sources of, 52 Data cleaning, 53 Data collection, 52 Data mining, 625–628 DCOVA, 34, 48 Decision trees, 196 Define, 34, 35, 48 Degrees of freedom, 306, 308 Dependent variable, 502 Descriptive statistics, 36 Deviance statistic, 588 Digital Case, 64, 121, 178, 211, 244, 275, 296, 330, 368, 410, 452, 494, 548, 597, 633, 679, 691 Directional test, 354 Discrete probability distributions binomial distribution, 225 covariance, 219–220 hypergeometric distribution, 236 Poisson distribution, 232 Discrete variables, 48 expected value of, 216–217 probability distribution for, 216 variance and standard deviation of, 217–218 Dispersion, 141 Downloading files for this book, 710 Drill-down, 109–110 Dummy variables, 576 Durbin-Watson statistic, 523–524 tables, 736 E Empirical probability, 187 Empirical rule, 163 Ethical issues confidence interval estimation and, 323 in hypothesis testing, 362–363 in multiple regression, 625 in numerical descriptive measures, 172 for probability, 206–207 for surveys, 59 Events, 187 Expected frequency, 461 Expected value, 216 of discrete variable, 217 of sum of two variables, 221 www.downloadslide.net Index Explained variation or regression sum of squares (SSR), 513–514 Explanatory variables, 502 Exponential distribution, 269 mean of, 269 standard deviation of, 269 Exponential growth with monthly data forecasting equation, 671 with quarterly data forecasting equation, 670 Exponential smoothing, 644–645 Exponential trend model, 650–651 Exponents, rules for, 696 Extrapolation, predictions in regression analysis and, 507 F Factor, 420 Factorial design See Two-way analysis of variance F distribution, 422 tables, 729–732 Finite population correction facvtor, 237 First-order autoregressive model, 657 First quartile, 154 Five-number summary, 156 Fixed effects model, 446 Forecasting, 640 autoregressive modeling for, 657–663 choosing appropriate autoregressive model for, 658–659 least-squares trend fitting and, 647–652 seasonal data, 669–673 Frame, 54 Frequency distribution, 76–78 F test for the ratio of two variances, 399–402 F test for factor A effect, 436 F test for factor B effect, 436 F test for the factor effect, 436 F test for interaction effect, 436 F test in one-way ANOVA, 422 F test for the slope, 527–528 G Gaussian distribution, 250 General addition rule, 191–192 General multiplication rule, 198–199 Geometric mean, 140 Geometric mean rate of return, 140 Grand mean, 421 Greek alphabet, 701 Groups, 420 Guidelines for developing visualizations, 106 H Histograms, 92–93 Homogeneity of variance, 428 Levene’s test for, 429–430 Homoscedasticity, 518 Hypergeometric distribution, 236 mean of, 237 standard deviation of, 237 Hypergeometric probabilities calculating, 236 Hypothesis See also One-sample tests of hypothesis alternative, 336 null, 336 tests of, 336 781 I Impossible event, 186 Independence, 197 of errors, 518 χ2 test of, 473–477 Independent events, multiplication rule for, 198 Independent variable, 502 Inferential statistics, 36 Interaction, 434 Interaction terms, 578–579 Interpolation, predictions in regression analysis and, 507 Interquartile range, 155 Interval scale, 49–50 Irregular effect, 641 J Joint event, 187 Joint probability, 189 Joint response, 72 Judgment sample, 55 K Kruskal-Wallis rank test for differences in c medians, 484–487 assumptions of, 484 Kurtosis, 149 L Lagged predictor variable, 657 Least-squares method in determining simple linear regression, 505 Least-squares trend fitting and forecasting, 647–652 Left-skewed, 148 Leptokurtic, 149 Level of confidence, 303 Level of significance (α), 339 Levels, 420 Levene’s test for homogeneity of variance, 429–430 Linear regression See Simple linear regression Linear relationship, 502 Linear trend model, 647–648 Linearity, 518 Logarithmic transformation, 613–614 Logarithms, rules for, 707 Logistic regression, 586–588 M Main effects, 439 Managing the Managing Ashland MultiComm Services, 63, 120, 178, 243, 274–275, 295, 329, 368, 409–410, 451–452, 493–494, 548, 597, 679 Marascuilo procedure, 470–471 Margin of error, 58 Marginal probability, 190 Matched samples, 385 Mathematical model, 225 McNemar test, 486 Mean, 136–139 of the binomial distribution, 230 confidence interval estimation for, 300–312 geometric, 140–141 of hypergeometric distribution, 237 population, 161, 281 sample size determination for, 317–319 www.downloadslide.net 782 Index Mean (continued) sampling distribution of, 280–281 standard error of, 282 unbiased property of, 280 Mean absolute deviation, 666 Mean square, 422 Mean Square A (MSA), 436 Mean Square Among (MSA), 422 Mean Square B (MSB), 436 Mean Square Error (MSE), 436 Mean Square Interaction (MSAB), 436 Mean Square Total (MST), 422 Mean Square Within (MSW), 422 Measurement levels of, 49–50 types of scales, 49–50 Measurement error, 58 Median, 138–139 Microsoft Excel absolute and relative cell references, 703 autogressive modeling, 682 bar charts, 126–127 basic probabilities, 213 Bayes’ theorem, 213 binomial probabilities, 246 bins for frequency distributions, 78–79 boxplots, 182 cell means plot, 457 cell references, 702 cells, 40 central tendency, 180 chart formatting, 706–707 check for the presence of the Analysis ToolPak and Solver Add-In, 721 checking for and applying Excel updates 718–719 chi-square tests for contingency tables, 497–498 coefficient of variation, 181 computing conventions, 42 confidence interval estimate for the difference between the means of two independent groups, 413 confidence interval for the mean, 332–333 confidence interval for the proportion, 333 configuring Excel security for Add-in usage, 719–720 contingency tables, 123–124 copying worksheets, 44 correlation coefficient, 183 covariance, 183 covariance of a probability distribution, 245 creating and copying worksheets, 44–45 creating histograms for discrete probability distributions, 708 cross-classification table, 123–124 cumulative percentage distribution, 126 cumulative percentage polygon, 130–131 deleting the “extra” bar from a histogram, 707 descriptive statistics, 180–181 dialog boxes, 43 enhancing workbook presentation, 706 entering array formulas, 704 entering data, 42–43 entering formulas into worksheets, 703–704 establishing the variable type, 66 expected value, 245 exponential probabilities, 277 exponential smoothing, 680 FAQs 745 five-number summary, 182 frequency distribution, 125 F test for the ratio of two variances, 417 geometric mean, 180–181 getting ready to use, 740–741 histogram, 129 hypergeometric probabilities, 247 interquartile range, 182 Kruskal-Wallis test, 499 least-squares trend fitting, 681–682 Levene test, 456 logistic regression, 601 Marascuilo procedure, 498 moving averages, 681 multidimensional contingency tables, 132–133 multiple regression, 598–600 mean absolute deviation, 682 new function names, 740–741 normal probabilities, 276 normal probability plot, 277 one-tail tests, 370 one-way analysis of variance, 454–455 opening workbooks, 43–44 ordered array, 124 quartiles, 181–182 paired t test, 415 Pareto chart, 127 Pasting with Paste Special, 704 percentage distribution, 126 percentage polygon, 130–131 pie chart, 126–127 PivotTables, 122–123, 132 Poisson probabilities, 246 pooled-variance t test, 412 population parameters, 182 portfolio expected return, 245 prediction interval, 552–553 preparing and using data, 43 printing worksheets, 45 probability, 213 probability distribution for a discrete random variable, 245 quadratic regression, 636 range, 181 recalculation, 702–703 recoding, 66 relative frequency distribution, 126 residual analysis, 551–552 sample size determination, 333 sampling distributions, 297 saving workbooks, 43–44 scatter plot, 131 selecting cell ranges for charts, 707 simple radom samples, 67 seasonal data, 683 separate-variance t test, 413 side-by-side bar chart, 128 simple linear regression, 550 slicers, 133 standard deviation, 181 summary tables, 122–123 templates, 40 time-series plot, 131–132 www.downloadslide.net Index transformations, 636 t test for the mean (σ unknown), 370 Tukey-Kramer multiple comparisons, 455 two-way analysis of variance, 456–457 understanding non-statistical functions, 742–743 useful keyboard shortcuts, 739 using a Visual Explorations Add-in workbook, 721 variance, 181 variance inflationary factor (VIF), 637 verifying formulas and worksheets, 740 Wilcoxon rank sum test, 499 workbooks, 40 worksheet entries and references, 702 worksheet formatting, 704–705 worksheets, 40 Z scores, 181 Z test for the difference between two proportions, 416 Z test for the mean (σ known), 369 Z test for the proportion, 371 Midspread, 155 Mixed effects model, 446 Mode, 139–140 Models See Multiple regression models More Descriptive Choices Follow-up, 121, 179, 211, 275, 331, 411, 453, 495, 635 Mountain States Potato Company case, 633 Moving averages, 642–643 Multidimensional contingency tables, 108–109 Multidimensional scaling, 627 Multiple comparisons, 426 Multiple regression models, 556 adjusted r, 562 best-subsets approach to, 619–622 coefficient of multiple determination in, 561–562 coefficients of partial determination in, 574 collinearity in, 615–616 confidence interval estimates for the slope in, 568–569 dummy-variable models in, 576 ethical considerations in, 625 interaction terms, 578–579 interpreting slopes in, 556–557 with k independent variables, 557 model building, 616–617 model validation, 623–624 net regression coefficients, 558 partial F-test statistic in, 570 pitfalls in, 624–625 predicting the dependent variable Y, 559 quadratic, 604–608 residual analysis for, 565–566 stepwise regression approach to, 618–619 testing portions of, 570–573 testing for significance of, 562–563 testing slopes in, 567–568 transformation in, 612–614 variance inflationary factors in, 615–616 Multiplication rule, 199 Mutually exclusive, 54 events, 190 N Net regression coefficient, 558 Neural nets, 627 Nominal scale, 49 Nonparametric methods, 479 Nonprobability sample, 54 Nonresponse bias, 58 Nonresponse error, 58 Normal approximation to the binomial distribution, 271 Normal distribution, 250 cumulative standardized, 253 properties of, 251 Normal probabilities calculating, 254–259 Normal probability density function, 252 Normal probability plot, 264 constructing, 264–265 Normality assumption, 428, 518 Null hypothesis, 336 Numerical data organizing, 75–83 visualizing, 92–97 Numerical descriptive measures coefficient of correlation, 167–170 measures of central tendency, variation, and shape, 136–150 from a population, 160–163 Numerical variables, 48 O Observed frequency, 461 Odds ratio, 586 Ogive, 96 One-tail tests, 354 null and alternative hypotheses in, 354 One-way analysis of variance (ANOVA), 420 assumptions, 428–429 F test for differences in more than two means, 422 F test statistic, 422 Levene’s test for homogeneity of variance, 429–430 Microsoft Excel for, 454–456 summary table, 423 Tukey-Kramer procedure, 426–427 Online topics and case files, 709 Operational definitions, 35 Ordered array, 75–76 Ordinal scale, 49–50 Organize, 34 Outliers, 53 Overall F test, 563 P Paired t test, 386 Parameter, 70 Pareto chart, 87–88 Pareto principle, 87 Parsimony, 617 Partial F-test statistic, 570 Percentage distribution, 79 Percentage polygon, 94–95 PHStat autocorrelation, 552 bar chart, 126 basic probabilities, 213 best subsets regression, 638 binomial probabilities, 246 boxplot, 182 783 www.downloadslide.net 784 Index PHStat (continued) cell means plot, 457 chi-square (χ2) test for contingency tables, 497–498 confidence interval for the difference between two means, 413 for the mean (σ known), 332 for the proportion, 333 configuring Excel for PHStat usage, 719 contingency tables, 123 covariance of a probability distribution, 245 cumulative percentage distributions, 126 cumulative polygons, 130–131 exponential probabilities, 277 FAQs, 744–745 frequency distributions, 124–125 F test for ratio of two variances, 417 getting ready to use, 719 histograms, 128–129 hypergeometric probabilities, 247 installing, 719–722 Kruskal-Wallis test, 499 kurtosis, 181 Levene’s test, 455 logistic regression, 601 Marascuilo procedure, 498 mean, 180 median, 180 mode, 180 model building, 638 multiple regression, 598–599 normal probabilities, 276 normal probability plot, 276 one-tail tests, 370 one-way ANOVA, 454 one-way tables, 122 opening, 720–721 paired t test, 414 Pareto chart, 126 percentage distribution, 126 percentage polygon, 130–131 pie chart, 126 Poisson probabilities, 246 pooled-variance t test, 412 portfolio expected return, 245 portfolio risk, 245 residual analysis, 551 sample size determination for the mean, 333 for the proportion, 333 sampling distributions, 297 scatter plot, 131 separate-variance t test, 413 side-by-side bar chart, 128 simple linear regression, 550–551 simple probability, 213 simple random samples, 67 skewness, 181 stacked data, 124 standard deviation, 181 stem-and-leaf display, 128 stepwise regression, 638 summary tables, 122 t test for the mean (σ unknown), 369 Tukey-Kramer procedure, 455 two-way ANOVA, 456 unstacked data, 124 variance inflationary factor (VIF), 636 Wilcoxon rank sum test, 498–499 Z test for the difference in two proportions, 416 Z test for the mean (σ known), 369 Z test for the proportion, 371 Pie chart, 86–87 PivotTables, 108 and business analytics, 110–112 Platykurtic, 149 Point estimate, 300 Poisson distribution, 232 calculating probabilities, 233 properties of, 232 Polygons, 94–95 cumulative percentage, 96–97 Pooled-variance t test, 374–379 Population(s), 53 Population mean, 161, 281 Population standard deviation, 162, 281 Population variance, 162 Portfolio, 221 Portfolio expected return, 221–222 Portfolio risk, 221–222 Power of a test, 339 Practical significance, 362–363 Prediction interval estimate, 535–536 Prediction line, 505 Predictive analytics, 625 Primary data source, 52 Probability, 186 a priori, 186 Bayes’ theorem for, 202 conditional, 194–196 empirical, 187 ethical issues and, 206–207 joint, 189 marginal, 190 simple, 188 subjective, 187 Probability density function, 250 Probability distribution for discrete random variable, 216 Probability distribution function, 225 Probability sample, 54 Proportions, 79–80 chi-square (χ2) test for differences between two, 460–465 chi-square (χ2) test for differences in more than two, 467–470 confidence interval estimation for, 314–316 sample size determination for, 319–321 sampling distribution of, 289–290 Z test for the difference between two, 393–395 Z test of hypothesis for, 412–361 pth-order autoregressive model, 658 p-value, 343 p-value approach, 344–345 Q Quadratic regression, 604–608 Quadratic trend model, 649–650 Qualitative forecasting methods, 640 www.downloadslide.net Index Qualitative variable, 48 Quantile-quantile plot, 264 Quantitative forecasting methods, 640 Quantitative variable, 48 Quartiles, 154–155 R Random effects model, 446 Random numbers, table of, 722–723 Randomized block design, 446 Randomness and independence, 428 Range, 141–142 interquartile, 155–156 Ratio scale, 50 Recoded variable, 53 Rectangular distribution, 266 Region of nonrejection, 338 Region of rejection, 338 Regression analysis See Multiple regression models; Simple linear regression Regression coefficients, 505 Relative frequency, 79–80 Relative frequency distribution, 79 Relevant range, 507 Repeated measurements, 385 Replicates, 434 Residual analysis, 518 Residual plots in detecting autocorrelation, 522–523 in evaluating equal variance, 520 in evaluating linearity, 518–519 in evaluating normality, 520 in multiple regression, 565–566 Residuals, 518 Resistant measures, 156 Response variable, 502 Right-skewed, 148 Robust, 351, 377 S Sample, 53 Sample mean, 136 Sample proportion, 289 Sample size determination for mean, 317–319 for proportion, 319–321 Sample space, 187 Sample standard deviation, 143 Sample variance, 143 Samples cluster, 56 convenience, 54 judgment, 55 nonprobability, 54 probability, 54 simple random, 55 stratified, 56 systematic, 56 Sampling from nonnormally distributed populations, 286–287 from normally distributed populations, 283–286 with replacement, 55 without replacement, 55 785 Sampling distributions, 280 of the mean, 280–281 of the proportion, 289–290 Sampling error, 58, 303 Scale interval, 50 nominal, 49 ordinal, 50 ratio, 50 Scatter diagram, 502 Scatter plot, 99–100, 502 Seasonal effect, 641 Secondary data source, 52 Selection bias, 58 Separate-variance t test for differences in two means, 380–382 Shape, 148 Side-by-side bar chart, 89 Simple event, 187 Simple linear regression, 502 assumptions in, 518 avoiding pitfalls in, 539 coefficient of determination in, 514–515 coefficients in, 505 computations in, 508–509 Durbin-Watson statistic, 523–524 equations in, 505 estimation of mean values and prediction of individual values, 534–536 inferences about the slope and correlation coefficient, 526–530 least-squares method in, 505 pitfalls in, 537 residual analysis, 518–521 standard error of the estimate in, 516–517 sum of squares in, 513–514 Simple probability, 188 Simple random sample, 55 Skewness, 148 Slope, 503 inferences about, 526–527, 557–558 interpreting, in multiple regression, 556–558 Sources of data, 52 Spread, 141 Square-root transformation, 612 Square roots, rules for, 696 Stacked data, 75 Standard deviation, 142–145 of binomial distribution, 230 of discrete random variable, 218 of hypergeometric distribution, 237 of population, 162 of sum of two random variables, 221 Standard error of the estimate, 516 Standard error of the mean, 282 Standard error of the proportion, 290 Standardized normal random variable, 252 Statistic, 70 Statistical inference, 36 Statistical symbols, 701 Statistics, 35 descriptive, 36 inferential, 36 Stem-and-leaf display, 92–93 Stepwise regression approach to model building, 618–619 www.downloadslide.net 786 Index Strata, 56 Stratified sample, 56 Student tips, 36, 70, 72, 80, 93, 139, 143, 146, 154, 186, 187, 191, 195, 216, 220, 225, 228, 252, 254, 255, 300, 305, 314, 341, 342, 344, 348, 349, 354, 358, 374, 375, 386, 393, 400, 420, 421, 422, 423, 426, 429, 436, 437, 461, 462, 470, 474, 479, 505, 506, 509, 514, 516, 518, 557, 558, 559, 562, 563, 565, 574, 604, 607, 610, 612, 642, 651, 662, 663, 667 Studentized range distribution, 427 tables, 734–735 Student’s t distribution, 306 Subjective probability, 187 Sum of squares, 142 Sum of squares among groups (SSA), 421 Sum of squares due to factor A (SSA), 435 Sum of squares due to factor B (SSB), 435 Sum of squares of error (SSE), 435, 514 Sum of squares to interaction (SSAB), 435 Sum of squares regression (SSR), 514 Sum of squares total (SST), 421, 435, 513 Sum of squares within groups (SSW), 421 Summary table, 71 Summation notation, 718–720 Sure Value Convenience Stores, 331, 368, 410, 452, 495, 633 Survey errors, 57–58 Symmetrical, 148 Systematic sample, 56 T Tables for categorical data, 71–73 chi-square, 728 contingency, 72–73, 188, 460 control chart factors, 737 cumulative standardized normal distribution, 724–725 Durbin-Watson, 736 F distribution, 729–732 for numerical data, 76–82 of random numbers, 55 standardized normal distribution, 738 Studentized range, 734–735 summary, 71 t distribution, 726–727 Wilcoxon rank sum, 733 t distribution, properties of, 307 Test statistic, 338 Tests of hypothesis Chi-square (χ2) test for differences between c proportions, 467–470 between two proportions, 460–465 Chi-square (χ2) test of independence, 473–477 F test for the ratio of two variances, 399–402 F test for the regression model, 563 F test for the slope, 527–528 Kruskal-Wallis rank test for differences in c medians, 484–487 McNemar test, 486 Levene test, 429–430 paired t test, 386–389 pooled-variance t test, 374–379 separate-variance t test for differences in two means, 380–382 t test for the correlation coefficient, 529–530 t test for the mean (σ unknown), 348–351 t test for the slope, 526–527, 567–568 Wilcoxon rank sum test for differences in two medians, 478–482 Z test for the difference between two proportions, 393–395 Z test for the mean (σ known), 340 Z test for the proportion, 358–361 Think About This, 59–60, 205, 261, 382, 540, 675 Third quartile, 154 Times series, 640 Time-series forecasting autoregressive model, 657–663 choosing an appropriate autoregressive model, 657–663 component factors of classical multiplicative, 640–641 exponential smoothing in, 644–645 least-squares trend fitting and forecasting, 647–652 moving averages in, 642–643 seasonal data, 669–673 Times series plot, 100–101 Total variation, 420–421, 513 Transformation formula, 252 Transformations in regression models logarithmic, 613–614 square-root, 612 Trend, 641 t test for a correlation coefficient, 529–530 t test for the mean (σ unknown), 348–351 t test for the slope, 526–527, 567–568 Tukey-Kramer multiple comparison procedure, 426–428 Tukey multiple comparison procedure, 440–441 Two-factor factorial design, 433 Two-sample tests of hypothesis for numerical data, 374 F tests for differences in two variances, 399–402 paired t test, 386–389 t tests for the difference in two means, 374–382 Wilcoxon rank sum test for differences in two medians, 478–482 Two-tail test, 341 Two-way analysis of variance cell means plot, 441 factorial design, 433 interpreting interaction effects, 441–443 Microsoft Excel for, 456–457 multiple comparisons, 440–441 Two-way contingency table, 460 Type I error, 338 Type II error, 338 U Unbiased, 280 Unexplained variation or error sum of squares (SSE), 513–514 Uniform probability distribution, 266 mean, 267 standard deviation, 267 Unstacked data, 75 V Variables, 35 categorical, 48 continuous, 48 discrete, 48 dummy, 576 numerical, 48 Variance, 142 of discrete random variable, 217 F-test for the ratio of two, 399–402 Levene’s test for homogeneity of, 429–430 www.downloadslide.net Index population, 162 sample, 142–143 of the sum of two random variables, 221 Variance inflationary factor (VIF), 615–616 Variation, 136 Visual Explorations descriptive statistics, 150 normal distribution, 260 sampling distributions, 288 simple linear regression, 510 Visualize, 34 W Wald statistic, 588 White test for homoscedasticity, 521 Width of class interval, 76–77 Wilcoxon rank sum test for differences in two medians, 478–482 Within-group variation, 420–421 Y Y intercept b0, 503 Z Z scores, 147 Z test for the difference between two proportions, 393–395 for the mean (σ known), 340 for the proportion, 358–361 787 www.downloadslide.net www.downloadslide.net www.downloadslide.net The Cumulative Standardized Normal Distribution Entry represents area under the cumulative standardized normal distribution from - ∞ to Z –∞ Z Cumulative Probabilities Z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 -6.0 -5.5 -5.0 -4.5 -4.0 -3.9 -3.8 -3.7 -3.6 -3.5 -3.4 -3.3 -3.2 -3.1 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 -0.0 0.000000001 0.000000019 0.000000287 0.000003398 0.000031671 0.00005 0.00007 0.00011 0.00016 0.00023 0.00034 0.00048 0.00069 0.00097 0.00135 0.0019 0.0026 0.0035 0.0047 0.0062 0.0082 0.0107 0.0139 0.0179 0.0228 0.0287 0.0359 0.0446 0.0548 0.0668 0.0808 0.0968 0.1151 0.1357 0.1587 0.1841 0.2119 0.2420 0.2743 0.3085 0.3446 0.3821 0.4207 0.4602 0.5000 0.00005 0.00007 0.00010 0.00015 0.00022 0.00032 0.00047 0.00066 0.00094 0.00131 0.0018 0.0025 0.0034 0.0045 0.0060 0.0080 0.0104 0.0136 0.0174 0.0222 0.0281 0.0351 0.0436 0.0537 0.0655 0.0793 0.0951 0.1131 0.1335 0.1562 0.1814 0.2090 0.2388 0.2709 0.3050 0.3409 0.3783 0.4168 0.4562 0.4960 0.00004 0.00007 0.00010 0.00015 0.00022 0.00031 0.00045 0.00064 0.00090 0.00126 0.0018 0.0024 0.0033 0.0044 0.0059 0.0078 0.0102 0.0132 0.0170 0.0217 0.0274 0.0344 0.0427 0.0526 0.0643 0.0778 0.0934 0.1112 0.1314 0.1539 0.1788 0.2061 0.2358 0.2676 0.3015 0.3372 0.3745 0.4129 0.4522 0.4920 0.00004 0.00006 0.00010 0.00014 0.00021 0.00030 0.00043 0.00062 0.00087 0.00122 0.0017 0.0023 0.0032 0.0043 0.0057 0.0075 0.0099 0.0129 0.0166 0.0212 0.0268 0.0336 0.0418 0.0516 0.0630 0.0764 0.0918 0.1093 0.1292 0.1515 0.1762 0.2033 0.2327 0.2643 0.2981 0.3336 0.3707 0.4090 0.4483 0.4880 0.00004 0.00006 0.00009 0.00014 0.00020 0.00029 0.00042 0.00060 0.00084 0.00118 0.0016 0.0023 0.0031 0.0041 0.0055 0.0073 0.0096 0.0125 0.0162 0.0207 0.0262 0.0329 0.0409 0.0505 0.0618 0.0749 0.0901 0.1075 0.1271 0.1492 0.1736 0.2005 0.2296 0.2611 0.2946 0.3300 0.3669 0.4052 0.4443 0.4840 0.00004 0.00006 0.00009 0.00013 0.00019 0.00028 0.00040 0.00058 0.00082 0.00114 0.0016 0.0022 0.0030 0.0040 0.0054 0.0071 0.0094 0.0122 0.0158 0.0202 0.0256 0.0322 0.0401 0.0495 0.0606 0.0735 0.0885 0.1056 0.1251 0.1469 0.1711 0.1977 0.2266 0.2578 0.2912 0.3264 0.3632 0.4013 0.4404 0.4801 0.00004 0.00006 0.00008 0.00013 0.00019 0.00027 0.00039 0.00056 0.00079 0.00111 0.0015 0.0021 0.0029 0.0039 0.0052 0.0069 0.0091 0.0119 0.0154 0.0197 0.0250 0.0314 0.0392 0.0485 0.0594 0.0721 0.0869 0.1038 0.1230 0.1446 0.1685 0.1949 0.2236 0.2546 0.2877 0.3228 0.3594 0.3974 0.4364 0.4761 0.00004 0.00005 0.00008 0.00012 0.00018 0.00026 0.00038 0.00054 0.00076 0.00107 0.0015 0.0021 0.0028 0.0038 0.0051 0.0068 0.0089 0.0116 0.0150 0.0192 0.0244 0.0307 0.0384 0.0475 0.0582 0.0708 0.0853 0.1020 0.1210 0.1423 0.1660 0.1922 0.2206 0.2514 0.2843 0.3192 0.3557 0.3936 0.4325 0.4721 0.00003 0.00005 0.00008 0.00012 0.00017 0.00025 0.00036 0.00052 0.00074 0.00103 0.0014 0.0020 0.0027 0.0037 0.0049 0.0066 0.0087 0.0113 0.0146 0.0188 0.0239 0.0301 0.0375 0.0465 0.0571 0.0694 0.0838 0.1003 0.1190 0.1401 0.1635 0.1894 0.2177 0.2482 0.2810 0.3156 0.3520 0.3897 0.4286 0.4681 0.00003 0.00005 0.00008 0.00011 0.00017 0.00024 0.00035 0.00050 0.00071 0.00100 0.0014 0.0019 0.0026 0.0036 0.0048 0.0064 0.0084 0.0110 0.0143 0.0183 0.0233 0.0294 0.0367 0.0455 0.0559 0.0681 0.0823 0.0985 0.1170 0.1379 0.1611 0.1867 0.2148 0.2451 0.2776 0.3121 0.3483 0.3859 0.4247 0.4641 www.downloadslide.net The Cumulative Standardized Normal Distribution (continued) Entry represents area under the cumulative standardized normal distribution from - ∞ to Z –∞ Z Cumulative Probabilities Z 0.00 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 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.5 5.0 5.5 6.0 0.5000 0.5398 0.5793 0.6179 0.6554 0.6915 0.7257 0.7580 0.7881 0.8159 0.8413 0.8643 0.8849 0.9032 0.9192 0.9332 0.9452 0.9554 0.9641 0.9713 0.9772 0.9821 0.9861 0.9893 0.9918 0.9938 0.9953 0.9965 0.9974 0.9981 0.99865 0.99903 0.99931 0.99952 0.99966 0.99977 0.99984 0.99989 0.99993 0.99995 0.999968329 0.999996602 0.999999713 0.999999981 0.999999999 0.5040 0.5438 0.5832 0.6217 0.6591 0.6950 0.7291 0.7612 0.7910 0.8186 0.8438 0.8665 0.8869 0.9049 0.9207 0.9345 0.9463 0.9564 0.9649 0.9719 0.9778 0.9826 0.9864 0.9896 0.9920 0.9940 0.9955 0.9966 0.9975 0.9982 0.99869 0.99906 0.99934 0.99953 0.99968 0.99978 0.99985 0.99990 0.99993 0.99995 0.5080 0.5478 0.5871 0.6255 0.6628 0.6985 0.7324 0.7642 0.7939 0.8212 0.8461 0.8686 0.8888 0.9066 0.9222 0.9357 0.9474 0.9573 0.9656 0.9726 0.9783 0.9830 0.9868 0.9898 0.9922 0.9941 0.9956 0.9967 0.9976 0.9982 0.99874 0.99910 0.99936 0.99955 0.99969 0.99978 0.99985 0.99990 0.99993 0.99996 0.5120 0.5517 0.5910 0.6293 0.6664 0.7019 0.7357 0.7673 0.7967 0.8238 0.8485 0.8708 0.8907 0.9082 0.9236 0.9370 0.9484 0.9582 0.9664 0.9732 0.9788 0.9834 0.9871 0.9901 0.9925 0.9943 0.9957 0.9968 0.9977 0.9983 0.99878 0.99913 0.99938 0.99957 0.99970 0.99979 0.99986 0.99990 0.99994 0.99996 0.5160 0.5557 0.5948 0.6331 0.6700 0.7054 0.7389 0.7704 0.7995 0.8264 0.8508 0.8729 0.8925 0.9099 0.9251 0.9382 0.9495 0.9591 0.9671 0.9738 0.9793 0.9838 0.9875 0.9904 0.9927 0.9945 0.9959 0.9969 0.9977 0.9984 0.99882 0.99916 0.99940 0.99958 0.99971 0.99980 0.99986 0.99991 0.99994 0.99996 0.5199 0.5596 0.5987 0.6368 0.6736 0.7088 0.7422 0.7734 0.8023 0.8289 0.8531 0.8749 0.8944 0.9115 0.9265 0.9394 0.9505 0.9599 0.9678 0.9744 0.9798 0.9842 0.9878 0.9906 0.9929 0.9946 0.9960 0.9970 0.9978 0.9984 0.99886 0.99918 0.99942 0.99960 0.99972 0.99981 0.99987 0.99991 0.99994 0.99996 0.5239 0.5636 0.6026 0.6406 0.6772 0.7123 0.7454 0.7764 0.8051 0.8315 0.8554 0.8770 0.8962 0.9131 0.9279 0.9406 0.9515 0.9608 0.9686 0.9750 0.9803 0.9846 0.9881 0.9909 0.9931 0.9948 0.9961 0.9971 0.9979 0.9985 0.99889 0.99921 0.99944 0.99961 0.99973 0.99981 0.99987 0.99992 0.99994 0.99996 0.5279 0.5675 0.6064 0.6443 0.6808 0.7157 0.7486 0.7794 0.8078 0.8340 0.8577 0.8790 0.8980 0.9147 0.9292 0.9418 0.9525 0.9616 0.9693 0.9756 0.9808 0.9850 0.9884 0.9911 0.9932 0.9949 0.9962 0.9972 0.9979 0.9985 0.99893 0.99924 0.99946 0.99962 0.99974 0.99982 0.99988 0.99992 0.99995 0.99996 0.5319 0.5714 0.6103 0.6480 0.6844 0.7190 0.7518 0.7823 0.8106 0.8365 0.8599 0.8810 0.8997 0.9162 0.9306 0.9429 0.9535 0.9625 0.9699 0.9761 0.9812 0.9854 0.9887 0.9913 0.9934 0.9951 0.9963 0.9973 0.9980 0.9986 0.99897 0.99926 0.99948 0.99964 0.99975 0.99983 0.99988 0.99992 0.99995 0.99997 0.5359 0.5753 0.6141 0.6517 0.6879 0.7224 0.7549 0.7852 0.8133 0.8389 0.8621 0.8830 0.9015 0.9177 0.9319 0.9441 0.9545 0.9633 0.9706 0.9767 0.9817 0.9857 0.9890 0.9916 0.9936 0.9952 0.9964 0.9974 0.9981 0.9986 0.99900 0.99929 0.99950 0.99965 0.99976 0.99983 0.99989 0.99992 0.99995 0.99997 www.downloadslide.net ... 15.6) Statistics for Managers Using Microsoft Excel SevenTH ediTion Global edition Statistics for Managers Using Microsoft Excel SevenTH ediTion Global edition david M Levine Department of Statistics. .. bestselling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for. .. add-in for Microsoft Excel and a co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics and Practical Statistics by Example Using Microsoft Excel