Statistics for manager using microsoft excel 8e levine

A Roadmap for Selecting a Statistical Method Data Analysis Task For Numerical Variables For Categorical Variables Describing a group or Ordered array, stem-and-leaf display, frequency Summary table, bar chart, pie several groups distribution, relative frequency distribution, chart, doughnut chart, Pareto chart percentage distribution, cumulative percentage (Sections 2.1 and 2.3) distribution, histogram, polygon, cumulative percentage polygon, sparklines, gauges, treemaps (Sections 2.2, 2.4, 2.6, 17.4) 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 (online 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 (online 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 12.4) Paired t test (Section 10.2) F test for the difference between two variances (Section 10.4) Z test for the difference between two proportions (Section 10.3) Chi-square test for the difference between two proportions (Section 12.1) McNemar test for two related samples (online Section 12.6) Comparing more than One-way analysis of variance for comparing several Chi-square test for differences two groups means (Section 11.1) among more than two proportions (Section 12.2) Kruskal-Wallis test (Section 12.5) Two-way analysis of variance (Section 11.2) Randomized block design (online Section 11.3) 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) Sparklines (Section 2.6) Contingency table, side-by-side bar chart, doughnut chart, ­PivotTables (Sections 2.1, 2.3, 2.6) Chi-square test of independence (Section 12.3) Analyzing the relationship between two or more variables Multiple regression (Chapters 14 and 15) Regression trees (Section 17.5) Multidimensional contingency ­tables (Section 2.6) Drilldown and slicers (Section 2.6) Logistic regression (Section 14.7) Classification trees (Section 17.5) This page intentionally left blank Statistics for Managers Using ® Microsoft Excel 8th 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 Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City S~ ao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editorial Director: Chris Hoag Editor in Chief: Deirdre Lynch Acquisitions Editor: Suzanna Bainbridge Senior Editorial Assistant: Justin Billing Program Manager: Chere Bemelmans Project Manager: Sherry Berg Program Management Team Lead: Karen Wernholm Project Management Team Lead: Peter Silvia Media Producer: Jean Choe TestGen Content Manager: John Flanagan QA Manager, Assessment Content: Marty Wright Senior MathXL Content Developer: Robert Carroll Marketing Manager: Tiffany Bitzel Field Marketing Manager: Adam Goldstein Marketing Assistant: Jennifer Myers Senior Author Support/Technology Specialist: Joe Vetere Manager, Rights Management: Gina Cheselka Senior Procurement Specialist: Carol Melville Associate Director of Design: Andrea Nix Program Design Lead and Cover Design: Barbara Atkinson Text Design: Cenveo Publisher Services Production 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SOFTWARE VERSION SPECIFIED Library of Congress Cataloging-in-Publication Data Levine, David M., 1946– author   Statistics for managers using Microsoft Excel / David M Levine, David F Stephan, Kathryn A Szabat.—8th edition   pages cm   Includes bibliographical references and index   ISBN 978-0-13-417305-4 (hardcover) Microsoft Excel (Computer file) Management—Statistical methods Commercial statistics Electronic spreadsheets Management—Statistical methods—Computer programs Commercial statistics—Computer programs I Stephan, David F., author II Szabat, Kathryn A., author III Title   HD30.215.S73 2017  519.50285'554—dc23 2015027139 10—CRK—19 18 17 16 15   www.pearsonhighered.com ISBN-10: 0-13-417305-8 ISBN-13: 978-0-13-417305-4 To our spouses and children, Marilyn, Sharyn, Mary, and Mark and to our parents, in loving memory, Lee, Reuben, Ruth, Francis, Mary, and William About the Authors David M Levine, David F Stephan, and Kathryn A Szabat are all experienced business school educators committed to innovation and improving instruction in business statistics and related subjects David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY, is a nationally recognized innovator in statistics education for more than three decades Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience Levine has presented or chaired numerous sessions about business eduKathryn Szabat, David Levine, and David Stephan cation at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Making Statistics More Effective in Schools and Business (MSMESB) mini-conference During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College’s highest distinguished teaching honor He earned B.B.A and M.B.A degrees from CCNY and a Ph.D in industrial engineering and operations research from New York University Advances in computing have always shaped David Stephan’s professional life As an undergraduate, he helped professors use statistics software that was considered advanced even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades) An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson’s MathXL and served as special assistant for computing to the Dean and Provost at Baruch College In his many years teaching at Baruch, Stephan implemented the first computer-based classroom, helped redevelop the CIS curriculum, and, as part of a FIPSE project team, designed and implemented a multimedia learning environment He was also nominated for teaching honors Stephan has presented at the SEDSI conference and the DSI MSMESB mini-conferences, sometimes with his coauthors Stephan earned a B.A from Franklin & Marshall College and an M.S from Baruch College, CUNY, and he studied instructional technology at Teachers College, Columbia University As Associate Professor of Business Systems and Analytics at La Salle University, Kathryn Szabat has transformed several business school majors into one interdisciplinary major that better supports careers in new and emerging disciplines of data analysis including analytics Szabat strives to inspire, stimulate, challenge, and motivate students through innovation and curricular enhancements, and shares her coauthors’ commitment to teaching excellence and the continual improvement of statistics presentations Beyond the classroom she has provided statistical advice to numerous business, nonbusiness, and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building Her research activities have led to journal publications, chapters in scholarly books, and conference presentations Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI MSMESB She received a B.S from SUNY-Albany, an M.S in statistics from the Wharton School of the University of Pennsylvania, and a Ph.D degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania For all three coauthors, continuous improvement is a natural outcome of their curiosity about the world Their varied backgrounds and many years of teaching experience have come together to shape this book in ways discussed in the Preface vi Brief Contents Preface xvii First Things First 1 Defining and Collecting Data  12 Organizing and Visualizing Variables  32 Numerical Descriptive Measures  95 Basic Probability  141 Discrete Probability Distributions  166 The Normal Distribution and Other Continuous Distributions  189 Sampling Distributions  216 Confidence Interval Estimation  237 Fundamentals of Hypothesis Testing: One-Sample Tests  270 10 Two-Sample Tests  307 11 Analysis of Variance  348 12 Chi-Square Tests and Nonparametric Tests  386 13 Simple Linear Regression  427 14 Introduction to Multiple Regression  475 15 Multiple Regression Model Building  521 16 Time-Series Forecasting  553 17 Getting Ready To Analyze Data In The Future  598 18 Statistical Applications in Quality Management (online)  18-1 19 Decision Making (online)  19-1 Appendices A–G  613 Self-Test Solutions and Answers to Selected Even-Numbered Problems  661 Index  692 Credits  699 vii Contents Preface xvii 1.4 Data Preparation  20 Data Cleaning  20 Data Formatting  21 Stacked and Unstacked Variables  21 Recoding Variables  22 First Things First  Using Statistics: “The Price of Admission”  1.5 Types of Survey Errors  23 Coverage Error  23 Nonresponse Error  23 Sampling Error  23 Measurement Error  24 Ethical Issues About Surveys  24 Now Appearing on Broadway and Everywhere Else  FTF.1  Think Differently About Statistics  Statistics: A Way of Thinking  Analytical Skills More Important than Arithmetic Skills  Statistics: An Important Part of Your Business Education  FTF.2  Business Analytics: The Changing Face of Statistics  “Big Data”  Structured Versus Unstructured Data  FTF.3  Getting Started Learning Statistics  Statistic 5 Can Statistics (pl., Statistic) Lie?  FTF.4  Preparing to Use Microsoft Excel for Statistics  Reusability Through Recalculation  Practical Matters: Skills You Need  Ways of Working with Excel  Excel Guides  Which Excel Version to Use?  Conventions Used  References 9 Key Terms  Excel Guide  10 EG.1 Entering Data  10 EG.2 Reviewing Worksheets  10 EG.3 If You Plan to Use the Workbook Instructions  11 Defining and Collecting Data  12 Consider This: New Media Surveys/Old Survey Errors  24 Using Statistics: Defining Moments, Revisited  26 Summary 26 References 26 Key Terms  26 Checking Your Understanding  27 Chapter Review Problems  27 Cases For Chapter 1 28 Managing Ashland MultiComm Services  28 CardioGood Fitness  28 Clear Mountain State Student Survey  29 Learning with the Digital Cases  29 Chapter Excel Guide  30 EG1.1 Defining Variables  30 EG1.2 Collecting Data  30 EG1.3 Types of Sampling Methods  31 Organizing and Visualizing Variables  32 Using Statistics: “The Choice Is Yours”  32 Using Statistics: Defining Moments  12 2.1 Organizing Categorical Variables  33 1.1 Defining Variables  13 Classifying Variables by Type  14 Measurement Scales  14 The Summary Table  33 The Contingency Table  34 2.2 1.2 Collecting Data  15 The Frequency Distribution  38 Classes and Excel Bins  40 The Relative Frequency Distribution and the Percentage Distribution 41 The Cumulative Distribution  43 Populations and Samples  16 Data Sources  16 1.3 Types of Sampling Methods  17 Simple Random Sample  18 Systematic Sample  18 Stratified Sample  19 Cluster Sample  19 viii Organizing Numerical Variables  37 2.3 Visualizing Categorical Variables  46 The Bar Chart  46 The Pie Chart and the Doughnut Chart  47 Contents The Pareto Chart  48 Visualizing Two Categorical Variables  50 The Variance and the Standard Deviation  102 EXHIBIT: Manually Calculating the Sample Variance, S2, and Sample Standard Deviation, S 103 The Coefficient of Variation  105 Z Scores  106 Shape: Skewness  108 Shape: Kurtosis  108 2.4 Visualizing Numerical Variables  52 The Stem-and-Leaf Display  53 The Histogram  54 The Percentage Polygon  55 The Cumulative Percentage Polygon (Ogive)  56 2.5 Visualizing Two Numerical Variables  59 3.3 Exploring Numerical Data  113 Quartiles 113 EXHIBIT: Rules for Calculating the Quartiles from a Set of Ranked Values  113 The Interquartile Range  115 The Five-Number Summary  115 The Boxplot  117 The Scatter Plot  59 The Time-Series Plot  61 2.6 Organizing and Visualizing a Mix of Variables  63 Multidimensional Contingency Table  63 Adding a Numerical Variable to a Multidimensional Contingency Table  64 Drill Down  64 Excel Slicers  65 PivotChart 66 Sparklines 66 2.7 The Challenge in Organizing and Visualizing Variables 68 Obscuring Data  68 Creating False Impressions  69 Chartjunk 70 EXHIBIT: Best Practices for Creating Visualizations  72 Using Statistics: The Choice Is Yours, Revisited  73 Summary 73 References 74 Key Equations  74 Key Terms  75 Checking Your Understanding  75 Chapter Review Problems  75 Cases For Chapter 2 80 Managing Ashland MultiComm Services  80 Digital Case  80 CardioGood Fitness  81 The Choice Is Yours Follow-Up  81 Clear Mountain State Student Survey   81 Chapter Excel Guide  82 EG2.1 Organizing Categorical Variables  82 EG2.2 Organizing Numerical Variables  84 EG2.3 Visualizing Categorical Variables  86 EG2.4 Visualizing Numerical Variables  88 EG2.5 Visualizing Two Numerical Variables  92 EG2.6 Organizing and Visualizing a Set of Variables  92 Numerical Descriptive Measures  95 3.4 Numerical Descriptive Measures for a Population 119 The Population Mean  120 The Population Variance and Standard Deviation  120 The Empirical Rule  121 Chebyshev’s Theorem  122 3.5 The Covariance and the Coefficient of Correlation  124 The Covariance  124 The Coefficient of Correlation  125 3.6 Statistics: Pitfalls and Ethical Issues  130 Using Statistics: More Descriptive Choices, Revisited 130 Summary 130 References 131 Key Equations 131 Key Terms  132 Checking Your Understanding  132 Chapter Review Problems  133 Cases For Chapter 3 136 Managing Ashland MultiComm Services  136 Digital Case  136 CardioGood Fitness  136 More Descriptive Choices Follow-up  136 Clear Mountain State Student Survey  136 Chapter Excel Guide  137 EG3.1 Central Tendency  137 EG3.2 Variation and Shape  138 EG3.3 Exploring Numerical Data  138 EG3.4 Numerical Descriptive Measures for a Population  139 EG3.5 The Covariance and the Coefficient of Correlation  139 Basic Probability  141 Using Statistics: More Descriptive Choices  95 Using Statistics: Possibilities at M&R Electronics World 141 3.1 Central Tendency  96 4.1 Basic Probability Concepts  142 The Mean  96 The Median  98 The Mode  99 The Geometric Mean  100 3.2 Variation and Shape  101 The Range  101 ix Events and Sample Spaces  143 Contingency Tables  145 Simple Probability  145 Joint Probability  146 Marginal Probability  147 General Addition Rule  147 Self-Test Solutions and Answers to Selected Even-Numbered Problems Intercept YLag1 YLag2 YLag3 Coefficients Standard Error t Stat P-value   0.4954   1.2924 - 0.7386   0.4769 0.1875 0.1774 0.2708 0.1905   2.6428   7.2852 -2.7272 2.503 0.0125 0.0000 0.0102 0.0174 Test of A3: p@value = 0.0174 0.05 Reject H0 that A3 = Third-order term cannot be deleted A third-order autoregressive model is appropriate Syx MAD Linear  1.9932  1.6967 Quadratic  0.6879  0.4338 Exponential 3.0371 1.6667 AR3 0.6194 0.4390 691 (h) The residuals in the first three models show strings of consecutive positive and negative values The autoregressive model performs well for the historical data and has a fairly random pattern of residuals It also has the smallest values in the standard error of the estimate and MAD Based on the principle of parsimony, the autoregressive model would probably be the best model for forecasting (i) Yn2015 = $28.3149 billions Index A a (level of significance), 274 A priori probability, 143 Addition rule, 148 Adjusted r2, 481–482 Algebra, rules for, 614 Alternative hypothesis, 271 Among-group variation, 350, Analysis of means (ANOM), 360 Analysis of proportions (ANOP), 399 Analysis of variance (ANOVA), 349 Kruskal-Wallis rank test for differences in c medians, 412–415 assumptions of, 415 One-way, assumptions, 356 F test for differences in more than two means, 352 F test statistic, 352 Levene’s test for homogeneity of variance, 357 summary table, 353 Tukey-Kramer procedure, 358–359 Two-way, 363 cell means plot, 371 factorial design, 363 interpreting interaction effects, 371–373 multiple comparisons, 369–370 summary table, 367 testing for factor and interaction effects, 364–369 Analysis ToolPak, Checking for presence, 637 Frequency distribution, 85 Histogram, 85, 86, 90 Descriptive statistics, 137 Exponential smoothing, 594 F test for ratio of two variances, 347 Multiple regression, 517–519 One-way ANOVA, 382 paired t test, 346 pooled-variance t test, 343–344 random sampling, 30 Residual analysis, 473 sampling distributions, 238 separate-variance t test, 344–345 simple linear regression, 473 two-way ANOVA, 384–385 Analyze, ANOVA See Analysis of variance (ANOVA) Area of opportunity, 178 Arithmetic mean See Mean Arithmetic operations, rules for, 614 692 Assumptions analysis of variance (ANOVA), 356 of the confidence interval estimate for the mean (s unknown), 245 of the confidence interval estimate for the proportion, 252, 254 of the F test for the ratio of two variances, 334 of the paired t test, 321 of Kruskal-Wallis test, 415 of regression, 443 for * table, 392 for * c table, 397 for r * c table, 405 for the t distribution, 245 t test for the mean (s unknown), 286–287 in testing for the difference between two means, 311 of the Wilcoxon rank sum test, 407 of the Z test for a proportion, 295 Autocorrelation, 447 Autoregressive modeling, steps involved in, on annual time-series data, 575–576 B Bar chart, 46–47 Bayes’ theorem, 159 Best-subsets approach in model building, 538–539 b Risk, 274 Bias nonresponse, 23 selection, 23 Big data, Binomial distribution, 171–177 mean of, 176 properties of, 171 shape of, 175 standard deviation of, 176 Binomial probabilities calculating, 173–175 Bootstraapping, 261 Boxplots, 117–118 Brynne packaging, 471 Business analytics, 4, 603 C CardioGood Fitness, 28–29, 81, 138, 164, 213, 267, 342, 381, 423 Categorical data chi-square test for the difference between two proportions, 387–392 chi-square test of independence, 400–405 chi-square test for c proportions, 394–397 organizing, 33–35 visualizing, 46–51 Z test for the difference between two proportions, 326–329 Categorical variables, 14 Causal forecasting methods, 554 Cell means plot, 371 Cell, Central limit theorem, 223 Central tendency, 96 Certain event, 142 Challenges in organizing and visualizing variables, Obscuring data, 68–69 Creating false impressions, 69–70 Chartjunk, 70–71 Charts bar, 46–47 doughnut, 47–48, 51 Pareto, 48–50 pie, 47–48 side-by-side bar, 50 Chebyshev Theorem, 122–123 Chi-square (x2) distribution, 388, Chi-square (x2) test for differences between c proportions, 394–397 between two proportions, 387–392 Chi-square (x2) test for the variance or standard deviation, 417 Chi-square (x2) test of independence, 400–405 Chi-square (x2) table, 645 Choice is Yours Followup, 81, 164 Class boundaries, 39 Class intervals, 38 Class midpoint, 40 Class interval width, 39 Classes, 38 and Excel bins, 40 Classification trees, 607–608 Clear Mountain State Survey, 29, 81, 138, 164, 213, 267, 342, 381, 423 Cluster analysis, 607 Cluster sample, 19 Coefficient of correlation, ,125–128 inferences about, 455 Coefficient of determination, 439–440 Coefficient of multiple determination, 481 Coefficient of partial determination, 493–494 Coefficient of variation, 105–106 Collectively exhaustive events, 147 Collect, Collinearity of independent variables, 534 Combinations, 172 Complement, 144 Index 693 Completely randomized design See One-way analysis of variance Conditional probability, 151–152 Confidence coefficient, 275 Confidence interval estimation, 238 connection between hypothesis testing and, 281–282 for the difference between the means of two independent groups, 313–314 for the difference between the proportions of two independent groups, 330 for the mean difference, 323–324 ethical issues and, 260–261 for the mean (s known), 241–243 for the mean (s unknown), 247–250 for the mean response, 458–459 for the proportion, 252–254 of the slope, 454, 487–488 Contingency tables, 34, 63, 145 Continuous probability distributions, 190 Continuous variables, 14 Convenience sampling, 17 Correlation coefficient See Coefficient of correlation Counting rules, 160 Covariance, 124–125 of a probability distribution, 181 Coverage error, 23 Cp statistic, 539 Craybill Instrumentation Company case, 549–550 Critical range, 358 Critical value approach, 276–279, 284–286, 290–291, 295 Critical values, 242, of test statistic, 272–273, 284–286 Cross-product term, 497 Cross validation, 542 Cumulative percentage distribution, 42–44 Cumulative percentage polygons, 56–57 Cumulative standardized normal distribution, 193 tables, 641–642 Cyclical effect, 555 D Dashboards, 605–606 Data, sources of, 15–16 Data cleaning, 20–21 Data collection, 15–17 Data formatting, 21 Data mining, 603–604 Data discovery, 63 DCOVA, 3, 33 Decision trees, 152–153 Define, Degrees of freedom, 244, 246, Dependent variable, 428 Descriptive analytics, 603–605 Descriptive statistics, Digital Case, 80, 138, 164, 186, 213, 235, 267, 304, 342, 381, 423, 471, 515– 516, 549 Directional test, 290 Discrete probability distributions binomial distribution, 171–177 Poisson distribution, 178 Discrete variables, 14 expected value of, 167–168 probability distribution for, 167 variance and standard deviation of, 169 Dispersion, 101 Doughnut chart, 47–48, 50–51 Downloading files for this book, 629 Drill-down, 64–66 Dummy variables, 495–497 Durbin-Watson statistic, 448–449 tables, 653 E Effect size, 336 Empirical probability, 143 Empirical rule, 121–122 Ethical issues confidence interval estimation and, 260–261 in hypothesis testing, 299 in multiple regression, 544 in numerical descriptive measures, 130 for probability, 156–157 for surveys, 24 Events, 143 Expected frequency, 388 Expected value, of discrete variable, 167–168 Explained variation or regression sum of squares (SSR), 438 Explanatory variables, 428 Exponential distribution, 190, 209 Exponential growth with monthly data forecasting equation, 584–588 with quarterly data forecasting equation, 584–588 Exponential smoothing, 558–560 Exponential trend model, 564–567 Extrapolation, predictions in regression analysis and, 433 F Factor, 349 Factorial design See Two-way analysis of variance F distribution, 352 tables, 646–649 Finite population correction factor, 261 First-order autoregressive model, 571–572 First quartile, 113 Five-number summary, 115–116 Fixed effects models, 375 Forecasting, 554 autoregressive modeling for, 571–578 choosing appropriate model for, 580–582 least-squares trend fitting and, 561–568 seasonal data, 583–588 Frame, 17 Frequency distribution, 38–40 F test for the ratio of two variances, 332–335 F test for the factor effect, 366 F test for factor A effect, 366 F test for factor B effect, 366 F test for interaction effect, 367 F test for the slope, 453 F test in one-way ANOVA, 352 G Gauges, 349, 605–606 General addition rule, 147–149 General multiplication rule, 155 Geometric mean, 100 Geometric mean rate of return, 100–101 Grand mean, 350 Greek alphabet, 619 Groups, 349 Guidelines for developing visualizations, 72 H Histograms, 53 Homogeneity of variance, 356 Levene’s test for, 357 Homoscedasticity, 443 Hypergeometric distribution, 182 Hypothesis See also One-sample tests of hypothesis alternative, 271 null, 271 I Impossible event, 142 Independence, 154 of errors, 443 x2 test of, 400–405 Independent events, multiplication rule for, 155 Independent variable, 428 Index numbers, 589 Inferential statistics, Interaction, 497 Interaction terms, 497 Interpolation, predictions in regression analysis and, 433 Interquartile range, 115 Interval scale, 14 Irregular effect, 555 J Joint probability, 146 Joint event, 144 Joint response, 34 Judgment sample, 17 K Kruskal-Wallis rank test for differences in c medians, 412–415 assumptions of, 415 Kurtosis, 108 694 Index L Lagged predictor variable, 571 Least-squares method in determining simple linear regression, 430–431 Least-squares trend fitting and forecasting, 561–568 Left-skewed, 108 Leptokurtic, 108 Level of confidence, 241 Level of significance (r), 274 Levels, 349 Levene’s test for homogeneity of variance, 357 Linear regression See Simple linear regression Linear relationship, 429 Linear trend model, 561–562 Logarithms, rules for, 615 Logarithmic transformation, 531–533 Logical causality, Logistic regression, 504–507 M Main effects, 369 Main effects plot, 371 Managing the Managing Ashland MultiComm Services, 28, 80, 138, 185–186, 213, 235, 266, 304, 341–342, 380–381, 422–423, 471, 515, 593 Marascuilo procedure, 397–399 Marginal probability, 147, 156 Margin of error, 23, 255 Matched samples, 318 Mathematical model, 171 McNemar test, 417 Mean, 96–98 of the binomial distribution, 176 confidence interval estimation for, 241– 243, 247–250 geometric, 100 population, 139 sample size determination for, 255–257 sampling distribution of, 217–227 standard error of, 219 unbiased property of, 217 Mean absolute deviation, 581 Mean difference, 318 Mean squares, 351 Mean Square Among (MSA), 351 Mean Square A (MSA), 366, Mean Square B (MSB), 366 Mean Square Error (MSE), 366, Mean Square Interaction (MSAB), 366 Mean Square Total (MST), 351 Mean Square Within (MSW), 351 Measurement types of scales, 14 Measurement error, 24 Median, 98–99 Microsoft Excel, absolute and relative cell references, 622 adding numericsl vriables, 64, 93 add-ins, 7, 637 array formulas, 623 arithmetic mean, 137 autogressive modeling, 595–596 bar charts, 86–87 Bayes’ theorem, 165 basic probabilities, 165 binomial probabilities, 187 bins, 40 boxplots, 139 cells, cell means plot, 385 cell references, 622 central tendency, 137 chart formatting, 625 chart sheets, checklist for using, 620 chi-square tests for contingency tables, 424–425 coefficient of variation, 138 confidence interval esimate for the difference between the means of two independent groups, 344 confidence interval for the mean, 268 confidence interval for the proportion, 269 contingency tables, 83–84 correlation coefficient, 140 covariance, ,139 creating histograms for discrete probability distributions, 627–628 creating and copying worksheets, cross-classification table, 83–84 cumulative percentage distribution, 86 cumulative percentage polygon, 91 descriptive statistics, 137–139 doughnut chart, 86–87 drilldown, 64–65 dummy variables, 519 entering data, 10 entering formulas into worksheets, 623 establishing the variable type, 30 expected value, 187 exponential smoothing, 594 FAQs, 660–661 formatting cell contents, 624 formatting cell elements, 624–625 formulas, 621 frequency distribution, 85 functions, 623 F test for the ratio of two variances, 347 gauges, 611 geometric mean, 137 Getting ready to use, 637 Guide workbooks, histogram, 89–90 interquartile range, 139 Kruskal-Wallis test, 426 kurtosis, 138 least-squares trend fitting, 595 Levene test, 383 logistic regression, 520 Marascuilo procedure, 424–425 median, 137 mode, 137 moving averages, 594 multidimensional contingency tables, 92–93 multiple regression, 517 mean absolute deviation, 596 model building, 552 new function names, 657 normal probabilities, 214 normal probability plot, 215 Office 365, 620 one-tail tests, 306 one-way analysis of variance, 382 opening workbooks, 621 ordered array, 84 quartiles, 138 paired t test, 345 Pareto chart, 87–88 pasting with Paste Special, 623 percentage distribution, 86 percentage polygon, 91 pie chart, 86–87 PivotChart, 66, 94 PivotTables, 63 Poisson probabilities, 188 pooled-variance t test, 343 population mean, 139 population standard deviation, 139 population variance, 139, Power Pivot, 611 prediction interval, 474 preparing and using data, printing worksheets, 621 probability, 165 probability distribution for a discrete random variable, quadratic regression, 551 range, 138 recalculation, 622 recoding, 30 relative frequency distribution, 86 residual analysis, 473, 518 reviewing worksheets, 10 sample size determination, 269 sampling distributions, 238 saving workbooks, 621 scatter plot, 92 seasonal data, 596 security settings, 637–638 separate-variance t test, 344 side-by-side bar chart, 88 simple linear regression, 472–474 simple random samples, 30 skewness, 138 skill set needed, slicers, 65, 94 sparklines, 67, 94 standard deviation, 138 stem-and-leaf display, 89 summary tables, 82–83 task pane, 11 t test for the mean (s unknown), 305 templates, 3, Index 695 Microsoft Excel, (continued) time-series plot, 92 transformations, 551–552 treemaps, 612 two-way analysis of variance, 384 Tukey-Kramer multiple comparisons, 383 understanding nonstatistical functions, 658–659 useful keyboard shortcuts, 656 variance, 138 variance inflationary factor (VIF), 552 verifying formulas and worksheets, 656 which version to use, 8, 620 Wilcoxon rank sum test, 425–426 workbooks, worksheet entries and references, 621 worksheets, Z test for the difference between two proportions, 346 Z test for the mean (s known), 305 Z scores, 138 Z test for the proportion, 306 Midspread, 115 Missing values, 20 Mixed effects models, 375 Mode, 99–100 Models See Multiple regression models More Descriptive Choices Follow-up, 138, 267, 342, 381, 423, 550 Mountain States Potato Company case, 548 Moving averages, 556–558 Multidimensional contingency tables, 63–64 Multidimensional scaling, 607 Multiple comparisons, 358 Multiple regression models, 476 Adjusted r, 481–482 best-subsets approach to, 538–539 coefficient of multiple determination in, 481, 527–528 coefficients of partial determination in, 493–494 collinearity in, 534 confidence interval estimates for the slope in, 487–488 dummy-variable models in, 495–497 ethical considerations in, 544 interpreting slopes in, 478 interaction terms, 497–501 with k independent variables, 477 model building, 535–541 model validation, 541–542 net regression coefficients, 478 overall F test, 482–483 partial F test statistic in, 489–493 pitfalls in, 544 predicting the dependent variable Y, 479 quadratic, 522–527 residual analysis for, 484–485 stepwise regression approach to, 537–538 testing for significance of, 482–483 testing portions of, 489–493 testing slopes in, 486–487 transformation in, 529–533 variance inflationary factors in, 534 Multiplication rule, 155 Mutually exclusive events, 147 N Net regression coefficient, 478 Nominal scale, 14 Nonparametric methods, 407 Nonprobability sample, 16 Nonresponse bias, 23 Nonresponse error, 23 Normal approximation to the binomial distribution, 209 Normal distribution, 190 cumulative standardized, 193 properties of, 191 Normal probabilities calculating, 193–201 Normal probability density function, 190 Normal probability plot, 205 constructing, 205 Normality assumption, 356, 443 Null hypothesis, 271 Numerical descriptive measures coefficient of correlation, 125–128 measures of central tendency, variation, and shape, 137–138 from a population, 120–121 Numerical variables, 14 Organizing, 37–44 Visualizing, 53–57 O Observed frequency, 388 Odds ratio, 504 Ogive, 56 One-tail tests, 290 null and alternative hypotheses in, 290 One-way analysis of variance (ANOVA), assumptions, 356 F test for differences among more than two means, 352 F test statistic, 334 Levene’s test for homogeneity of variance, 357 summary table, 353 Tukey-Kramer procedure, 358–359 Online resources, 629–636 Operational definitions, 5, 13 Ordered array, 37 Ordinal scale, 14 Organize, Outliers, 21, 106 Overall F test, 482–483 P Paired t test, 318–323 Parameter, 16 Pareto chart, 48–50 Pareto principle, 48 Parsimony, principle of, 581 Partial F-test statistic, 489–493 Percentage distribution, 41–42 Percentage polygon, 54–56 Percentiles, 114 PHStat, 7, 638 autocorrelation, 474 bar chart, 86 basic probabilities, 165 best subsets regression, 552 binomial probabilities, 187 boxplot, 139 cell means plot, 385 chi-square (x2) test for contingency tables, 424–425 confidence interval for the mean (s known), 268 for the mean (s unknown), 268 for the difference between two means, 344 for the mean value, 474 for the proportion, 269 contingency tables, 83 cumulative percentage distributions, 85–86 cumulative percentage polygons, 91 F test for ratio of two variances, 347 frequency distributions, 84 histograms, 89 Kruskal-Wallis test, 426 kurtosis, 138 Levene’s test, 383 logistic regression, 520 Marascuilo procedure, 424 mean, 137 median, 137 mode, 137 model building, 552 multiple regression, 517–519 normal probabilities, 214 normal probability plot, 215 one-way ANOVA, 382 one-way tables, 82 one-tail tests, 306 paired t test, 345 Pareto chart, 87 percentage distribution, 85–86 percentage polygon, 91 pie chart, 86 Poisson probabilities, 188 pooled-variance t test, 343 prediction interval, 474 quartiles, 138 random sampling, 31 range, 138 relative frequency, 85–86 residual analysis, 473 sample size determination, for the mean, 269 for the proportion, 269 sampling distributions, 238 scatter plot, 92 separate-variance t test, 344 side-by-side bar chart, 88 simple linear regression, 472–474 696 Index simple probability, 165 simple random samples, 30 skewness, 138 stacked data, 84 standard deviation, 138 stem-and-leaf display, 88 stepwise regression, 552 summary tables, 82 t test for the mean (s unknown), 305 two-way ANOVA, 384 Tukey-Kramer procedure, 383 unstacked data, 84 Wilcoxon rank sum test, 425 Z test for the mean (s known), 305 Z test for the difference in two proportions, 346 Z test for the proportion, 306 Pie chart, 47–48 PivotChart, 66 PivotTables, 63 Platykurtic, 108 Point estimate, 238 Poisson distribution, 178 calculating probabilities, 179–180 properties of, 179 Polygons, cumulative percentage, 56 Pooled-variance t test, 308–313 Population(s), 16 Population mean, 120, 218 Population standard deviation, 120–121, 218 Population variance, 120–121 Power of a test, 275, 300 Power Pivot, 604 Practical significance, 299 Prediction interval estimate, 459–460 Prediction line, 430 Predictive analytics, 603, 607–608 Prescriptive analytics, 603 Primary data source, 16 Probability, 142 a priori, 143 Bayes’ theorem for, 159 conditional, 151 empirical, 143 ethical issues and, 156–157 joint, 146 marginal, 147 simple, 143 subjective, 143 Probability density function, 193 Probability distribution function, 171 Probability distribution for discrete random variable, 167 Probability sample, 17 Proportions, chi-square (x2) test for differences between two, 387–392 chi-square (x2) test for differences in more than two, 394–397 confidence interval estimation for, 252–254 sample size determination for, 257–259 sampling distribution of, 228–230 Z test for the difference between two, 326–329 Z test of hypothesis for, 294–297 pth-order autoregressive model, 571 p-value, 279 p-value approach, 279–281, 286, 291–293, 296–297 Q Quadratic regression, 522–527 Quadratic trend model, 563–564 Qualitative forecasting methods, 554 Qualitative variable, 14 Quantitative forecasting methods, 555 Quantitative variable, 14 Quartiles, 113 Quantile-quantile plot, 205 R Random effect, 555 Random effects models, 375 Randomized block design 375 Randomness and independence, 356 Random numbers, table of, 639–640 Range, 101–102 interquartile, 115 Ratio scale, 14 Recoded variable, 22 Rectangular distribution, 193 Region of nonrejection, 273 Region of rejection, 273 Regression analysis See Multiple regression models; Simple linear regression Regression coefficients, 431–432, 476–477 Regression trees, 607 Relative frequency, 41 Relative frequency distribution, 41–42 Relevant range, 433 Repeated measurements, 317 Replicates, 364 Residual analysis, 443, 484–485, 580 Residual plots in detecting autocorrelation, 447–448 in evaluating equal variance, 446 in evaluating linearity, 444 in evaluating normality, 445 in multiple regression, 485 Residuals, 443 Resistant measures, 115 Response variable, 428 Right-skewed, 108 Robust, 287 S Sample, 16 Sample mean, 96 Sample proportion, 229, 294 Sample standard deviation, 103 Sample variance, 102 Sample size determination for mean, 255–257 for proportion, 257–259 Sample space, 144 Samples, 16 cluster, 19 convenience, 17 judgment, 17 nonprobability, 17 probability, 17 simple random, 18 stratified, 19 systematic, 18–19 Sampling from nonnormally distributed populations, 223–227 from normally distributed populations, 220–223 with replacement, 18 without replacement, 18 Sampling distributions, 217 of the mean, 217–227 of the proportion, 228–230 Sampling error, 23, 241 Scale interval, 14 nominal, 14 ordinal, 14 ratio, 14 Scatter diagram, 428 Scatter plot, 59–60, 428 Seasonal effect, 555–572 Second-order autocorrelation, 571 Second quartile, 113 Secondary data source, 16 Selection bias, 23 Separate-variance t test for differences in two means, 314 Shape, 96 Side-by-side bar chart, 50 Simple event, 143 Simple linear regression, assumptions in, 443 coefficient of determination in, 440 coefficients in, 431–432 computations in, 433–435 Durbin-Watson statistic, 448–449 equations in, 429, 431 estimation of mean values and prediction of individual values, 458–461 inferences about the slope and correlation coefficient, 451–455 least-squares method in, 430–431 pitfalls in, 462 residual analysis, 443–446 standard error of the estimate in, 441–442 sum of squares in, 438–439 Simple probability, 145 Simple random sample, 18 Skewness, 108 Slicers, 65–66 Slope, 430 inferences about, 451–455 interpreting, in multiple regression, 478 Index 697 Solver add-in, Checking for presence, 637 Sources of data, 16 Sparklines, 66–67 Spread, 101 Square-root transformation, 529–531 Stacked variables, 21 Standard deviation, 102–103 of binomial distribution, 176 of discrete random variable, 169 of population, 120–121 Standard error of the estimate, 441–442 Standard error of the mean, 219 Standard error of the proportion, 229 Standardized normal random variable, 192 Statistic, 5, 18 Statistics, 2, descriptive, inferential, Statistical inference, Statistical symbols, 619 Stem-and-leaf display, 53 Stepwise regression approach to model building, 537–538 Strata, 19 Stratified sample, 19 Structured data, Studentized range distribution, 358 tables, 651–652 Student’s t distribution, 244–245 Properties, 245 Student tips, 13, 14, 20, 22, 33, 39, 43, 53, 69, 98, 103, 105, 113, 118, 142, 143, 144, 147, 151, 167, 171, 192, 194, 195, 219, 228, 229, 238, 243, 252, 271, 273, 276, 277, 279, 284, 290, 294, 308, 309, 318, 326, 332, 349, 350, 351, 352, 354, 356, 357, 358, 365, 367, 388, 389, 398, 401, 407, 410, 413, 431, 432, 435, 440, 441, 444, 477, 478479, 481, 482, 485, 493, 496, 498, 505, 507, 522, 525, 527, 531, 556, 558, 565, 572, 576 Subjective probability, 143 Summary table, 33 Summation notation, 616–619 Sum of squares, 102 Sum of squares among groups (SSA), 351 Sum of squares due to factor A (SSA), 364 Sum of squares due to factor B (SSB), 365 Sum of squares due to regression (SSR), 439 Sum of squares of error (SSE), 365, 439, Sum of squares to interaction (SSAB), 365 Sum of squares total (SST), 350, 364, 438 Sum of squares within groups (SSW), 351 SureValue Convenience Stores, 267, 304, 342, 381, 423 Survey errors, 23–24 Symmetrical, 108 Systematic sample, 18–19 T Treemaps, 606 Tables chi-square, 645 contingency, 34 Control chart factors, 654 Durbin-Watson, 653 F distribution, 646–649 for categorical data, 33–35 cumulative standardized normal distribution, 641–642 of random numbers, 18, 639–640 standardized normal distribution, 655 Studentized range, 651–652 summary, 33 t distribution, 643–644 Wilcoxon rank sum, 650 t distribution, properties of, 245 Test statistic, 273 Tests of hypothesis Chi-square (x2) test for differences between c proportions, 394–397 between two proportions, 387–392 Chi-square (x2) test of independence, 400–405 F test for the ratio of two variances, 332–335 F test for the regression model, 482–483 F test for the slope, 453 Kruskal-Wallis rank test for differences in c medians, 410–415 Levene test, 357 Paired t test, 318–323 pooled-variance t test, 308–313 quadratic effect, 525–526 separate-variance t test for differences in two means, 314 t test for the correlation coefficient, 455 t test for the mean (s unknown), 284–287 t test for the slope, 451–452, 486–487 Wilcoxon rank sum test for differences in two medians, 406–410 Z test for the mean (s known), 276–281 Z test for the difference between two proportions, 326–329 Z test for the proportion, 294–297 Third quartile, 113 Times series, 554 Time-series forecasting autoregressive model, 571–579 choosing an appropriate forecasting model, 580–582 component factors of classical multiplicative, 554–555 exponential smoothing in, 559–560 least-squares trend fitting and forecasting, 561–568 moving averages in, 556–558 seasonal data, 583–588 Times series plot, 61 Total variation, 350, 438 Transformation formula, 193 Transformations in regression models logarithmic, 531–533 square-root, 529–531 Treatment, 16 Treemap, 606 Trend, 555 t test for a correlation coefficient, 455 t test for the mean (s unknown), 284–287 t test for the slope, 451–452, 486–487 Tukey-Kramer multiple comparison procedure, 358–359 Tukey multiple comparison procedure, 369–370 Two-factor factorial design, 363 Two-sample tests of hypothesis for numerical data, F tests for differences in two variances, 332–335 Paired t test, 318–323 t tests for the difference in two means, 308–315 Wilcoxon rank sum test for differences in two medians, 406–410 Two-tail test, 276 Two-way analysis of variance cell means plot, 371 factorial design, 363 interpreting interaction effects, 371–373 multiple comparisons, 369–370 summary table, 367 testing for factor and interaction effects, 364–369 Two-way contingency table, 387 Type I error, 274 Type II error, 274 U Unbiased, 217 Unexplained variation or error sum of squares (SSE), 365, 439 Uniform probability distribution, 193 mean, 207 standard deviation, 207 Unstacked variables, 21 Unstructured data, V Variables, categorical, 14 continuous, 14 discrete, 14 dummy, 495–497 numerical, 14 Variance inflationary factor (VIF), 534 698 Index Variance, of discrete random variable, 168 F-test for the ratio of two, 332–335 Levene’s test for homogeneity of, 357 population, 120–121 sample, 102 Variation, 96 Visual Explorations, normal distribution, 198 sampling distributions, 227 simple linear regression, 436 Visualize, 3, Visualizations, Guidelines for constructing, 72 W Wald statistic, 507 Width of class interval, 39 Wilcoxon rank sum test for differences in two medians, 406–410 Tables, 650 Within-group variation, 350 X Y Y intercept, 430 Z Z scores, 106–107 Z test, for the difference between two proportions, 326–329 for the mean (s known), 276–281 for the proportion, 294–297 Credits Front matter Chapter 11 First Things First Chapter 12 Page vi, Courtesy of David Levine Page 1, Wallix/iStock/Getty Images; page 2, Excerpt from Ticket Pricing Puts ‘Lion King’ Atop Broadway’s Circle Of Life by Patrick Healy Published by The New York Times © 2014 Chapter Pages 12 and 26, Haveseen/YAY Micro/AGE Fotostock Chapter Pages 32 and 73, Scanrail/123RF Chapter Pages 95 and 130, Gitanna/Fotolia Chapter Pages 141 and 161, Blue Jean Images/Collage/Corbis Chapter Pages 166 and 182, Hongqi Zhang/123RF Chapter Pages 189 and 210, Cloki/Shutterstock; screen image from The Adventures of Dirk “Sunny” Lande appears courtesy of Waldowood Productions Chapter Pages 216 and 231, Bluecinema/E+/Getty Images Chapter Pages 237 and 261, Mark Hunt/Huntstock/Corbis Chapter Pages 270 and 300, Ahmettozar/iStock/Getty Images Chapter 10 Pages 307 and 337, Echo/Cultura/Getty Images Pages 348 and 375, Paul Morris/Bloomberg/Getty Images Pages 386 and 418, Vibrant Image Studio/Shutterstock Chapter 13 Pages 427 and 464, Hero Images/Hero Images/Corbis Chapter 14 Pages 475 and 509, Maridav/123RF Chapter 15 Pages 521 and 544, Antbphotos/Fotolia Chapter 16 Pages 553 and 589, Stylephotographs/123RF Chapter 17 Pages 598 and 608, Courtesy of Sharyn Rosenberg Online Chapter 18 Pages 18-1 and 18-31, Zest_marina/Fotolia; Figure 18.9, From The Deming Route to Quality and Productivity: Road Maps and Roadblocks by William W Scherkenbach Copyright by CEEP Press Books Used by permission of CEEP Press Books Online Chapter 19 Pages 19-1 and 19-22, Ken Mellott/Shutterstock Appendix E Table E.09, From ASTM-STP 15D Copyright by ASTM International Used by permission of ASTM International Appendix F Excerpt from Microsoft Corporation, Function Improvements In Microsoft Office Excel 2010 Published by Microsoft Corporation 699 This page intentionally left blank Available with MyStatLab™ for Your Business Statistics Courses MyStatLab is the market-leading online learning management program for learning and teaching business statistics Statistical Software Support Built-in tutorial videos and functionality make using the most popular software solutions seamless and intuitive Tutorial videos, study cards, and manuals (for select titles) are available within MyStatLab and accessible at the point of use Easily launch exercise and eText data sets into Excel or StatCrunch, or copy and paste into any other software program Leverage the Power of StatCrunch MyStatLab leverages the power of StatCrunch –powerful, web-based statistical software In addition, access to the full online community allows users to take advantage of a wide variety of resources and applications at www.statcrunch.com Bring Statistics to Life Virtually flip coins, roll dice, draw cards, and interact with animations on your mobile device with the extensive menu of experiments and applets in StatCrunch Offering a number of ways to practice resampling procedures, such as permutation tests and bootstrap confidence intervals, StatCrunch is a complete and modern solution www.mystatlab.com This page intentionally left blank This page intentionally left blank The Cumulative Standardized Normal Distribution Entry represents area under the cumulative standardized normal distribution from - ∞ to Z 2` Z Cumulative Probabilities Z - 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 0.00 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 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 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 The Cumulative Standardized Normal Distribution (continued) Entry represents area under the cumulative standardized normal distribution from - ∞ to Z 2` Z Cumulative Probabilities Z 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 0.00 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 0.01 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 0.02 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 0.03 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 0.04 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 0.05 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 0.06 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 0.07 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 0.08 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 0.09 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 ... edition of Statistics for Managers Using Microsoft Excel continues the use of the following distinctive features Using Statistics Business Scenarios  Each chapter begins with a Using Statistics. .. pages xxiii and xxiv) David M Levine David F Stephan Kathryn A Szabat Resources for Success MyStatLab™ Online Course for Statistics for Managers Using Microsoft? ? Excel by Levine/ Stephan/Szabat (access... 17.5) This page intentionally left blank Statistics for Managers Using ® Microsoft Excel 8th Edition David M Levine Department of Statistics and Computer Information Systems Zicklin School of Business,