Essentials of statistics for bussiness and economics anderson

CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION Entries in this table give the area under the curve to the left of the z value For example, for z = –.85, the cumulative probability is 1977 Cumulative probability z z 00 01 02 03 04 05 06 07 08 09 Ϫ3.0 0013 0013 0013 0012 0012 0011 0011 0011 0010 0010 Ϫ2.9 Ϫ2.8 Ϫ2.7 Ϫ2.6 Ϫ2.5 0019 0026 0035 0047 0062 0018 0025 0034 0045 0060 0018 0024 0033 0044 0059 0017 0023 0032 0043 0057 0016 0023 0031 0041 0055 0016 0022 0030 0040 0054 0015 0021 0029 0039 0052 0015 0021 0028 0038 0051 0014 0020 0027 0037 0049 0014 0019 0026 0036 0048 Ϫ2.4 Ϫ2.3 Ϫ2.2 Ϫ2.1 Ϫ2.0 0082 0107 0139 0179 0228 0080 0104 0136 0174 0222 0078 0102 0132 0170 0217 0075 0099 0129 0166 0212 0073 0096 0125 0162 0207 0071 0094 0122 0158 0202 0069 0091 0119 0154 0197 0068 0089 0116 0150 0192 0066 0087 0113 0146 0188 0064 0084 0110 0143 0183 Ϫ1.9 Ϫ1.8 Ϫ1.7 Ϫ1.6 Ϫ1.5 0287 0359 0446 0548 0668 0281 0351 0436 0537 0655 0274 0344 0427 0526 0643 0268 0336 0418 0516 0630 0262 0329 0409 0505 0618 0256 0322 0401 0495 0606 0250 0314 0392 0485 0594 0244 0307 0384 0475 0582 0239 0301 0375 0465 0571 0233 0294 0367 0455 0559 Ϫ1.4 Ϫ1.3 Ϫ1.2 Ϫ1.1 Ϫ1.0 0808 0968 1151 1357 1587 0793 0951 1131 1335 1562 0778 0934 1112 1314 1539 0764 0918 1093 1292 1515 0749 0901 1075 1271 1492 0735 0885 1056 1251 1469 0721 0869 1038 1230 1446 0708 0853 1020 1210 1423 0694 0838 1003 1190 1401 0681 0823 0985 1170 1379 Ϫ.9 Ϫ.8 Ϫ.7 Ϫ.6 Ϫ.5 1841 2119 2420 2743 3085 1814 2090 2389 2709 3050 1788 2061 2358 2676 3015 1762 2033 2327 2643 2981 1736 2005 2296 2611 2946 1711 1977 2266 2578 2912 1685 1949 2236 2546 2877 1660 1922 2206 2514 2843 1635 1894 2177 2483 2810 1611 1867 2148 2451 2776 Ϫ.4 Ϫ.3 Ϫ.2 Ϫ.1 Ϫ.0 3446 3821 4207 4602 5000 3409 3783 4168 4562 4960 3372 3745 4129 4522 4920 3336 3707 4090 4483 4880 3300 3669 4052 4443 4840 3264 3632 4013 4404 4801 3228 3594 3974 4364 4761 3192 3557 3936 4325 4721 3156 3520 3897 4286 4681 3121 3483 3859 4247 4641 www.ebookslides.com CUMULATIVE PROBABILITIES FOR THE STANDARD NORMAL DISTRIBUTION Cumulative probability Entries in the table give the area under the curve to the left of the z value For example, for z = 1.25, the cumulative probability is 8944 z z 00 01 02 03 04 05 06 07 08 09 5000 5398 5793 6179 6554 5040 5438 5832 6217 6591 5080 5478 5871 6255 6628 5120 5517 5910 6293 6664 5160 5557 5948 6331 6700 5199 5596 5987 6368 6736 5239 5636 6026 6406 6772 5279 5675 6064 6443 6808 5319 5714 6103 6480 6844 5359 5753 6141 6517 6879 6915 7257 7580 7881 8159 6950 7291 7611 7910 8186 6985 7324 7642 7939 8212 7019 7357 7673 7967 8238 7054 7389 7704 7995 8264 7088 7422 7734 8023 8289 7123 7454 7764 8051 8315 7157 7486 7794 8078 8340 7190 7517 7823 8106 8365 7224 7549 7852 8133 8389 1.0 1.1 1.2 1.3 1.4 8413 8643 8849 9032 9192 8438 8665 8869 9049 9207 8461 8686 8888 9066 9222 8485 8708 8907 9082 9236 8508 8729 8925 9099 9251 8531 8749 8944 9115 9265 8554 8770 8962 9131 9279 8577 8790 8980 9147 9292 8599 8810 8997 9162 9306 8621 8830 9015 9177 9319 1.5 1.6 1.7 1.8 1.9 9332 9452 9554 9641 9713 9345 9463 9564 9649 9719 9357 9474 9573 9656 9726 9370 9484 9582 9664 9732 9382 9495 9591 9671 9738 9394 9505 9599 9678 9744 9406 9515 9608 9686 9750 9418 9525 9616 9693 9756 9429 9535 9625 9699 9761 9441 9545 9633 9706 9767 2.0 2.1 2.2 2.3 2.4 9772 9821 9861 9893 9918 9778 9826 9864 9896 9920 9783 9830 9868 9898 9922 9788 9834 9871 9901 9925 9793 9838 9875 9904 9927 9798 9842 9878 9906 9929 9803 9846 9881 9909 9931 9808 9850 9884 9911 9932 9812 9854 9887 9913 9934 9817 9857 9890 9916 9936 2.5 2.6 2.7 2.8 2.9 9938 9953 9965 9974 9981 9940 9955 9966 9975 9982 9941 9956 9967 9976 9982 9943 9957 9968 9977 9983 9945 9959 9969 9977 9984 9946 9960 9970 9978 9984 9948 9961 9971 9979 9985 9949 9962 9972 9979 9985 9951 9963 9973 9980 9986 9952 9964 9974 9981 9986 3.0 9987 9987 9987 9988 9988 9989 9989 9989 9990 9990 iStock.com/alienforce; iStock.com/TommL Essentials of Statistics for  Business and Economics 9e James J Cochran Thomas A Williams David R Anderson University of Alabama Rochester Institute of Technology University of Cincinnati Dennis J Sweeney Michael J Fry University of Cincinnati Jeffrey D Camm University of Cincinnati Jeffrey W Ohlmann Wake Forest University University of Iowa Australia    Brazil    Mexico    Singapore    United Kingdom    United States ● ● ● ● ● www.ebookslides.com Essentials of Statistics for Business and Economics, 9e © 2020, 2017 Cengage Learning, Inc David R Anderson Dennis J Sweeney Thomas A Williams Jeffrey D Camm James J Cochran Michael J Fry Jeffrey W Ohlmann Unless otherwise noted, all content is © Cengage WCN: 02-300 ALL RIGHTS RESERVED No part of this work covered by the copyright herein may be reproduced or distributed in any form or by any means, except as permitted by U.S copyright law, without the prior written permission of the copyright owner Senior Vice President, Higher Ed Product, For product information and technology assistance, contact us at Content, and Market Development: Erin Joyner Cengage Customer & Sales Support, 1-800-354-9706 or Senior Product Team Manager: Joe Sabatino support.cengage.com Senior Product Manager: Aaron Arnsparger For permission to use material from this text or product, submit all requests online at www.cengage.com/permissions Project Manager: John Rich Content Manager: Conor Allen Product Assistant: Renee Schnee Marketing Manager: Chris Walz Library of Congress Control Number: 2018967096 ISBN: 978-0-357-04543-5 Production Service: MPS Limited Designer, Creative Studio: Chris Doughman Text Designer: Beckmeyer Design Cover Designer: Beckmeyer Design Cengage 20 Channel Center Street Boston, MA 02210 USA Intellectual Property Analyst: Reba Frederics Cengage is a leading provider of customized learning solutions with Intellectual Property Project Manager: Nick than 125 countries around the world Find your local representative at Barrows employees residing in nearly 40 different countries and sales in more www.cengage.com Cengage products are represented in Canada by Nelson Education, Ltd To learn more about Cengage platforms and services, register or access your online learning solution, or purchase materials for your course, visit www.cengage.com Printed in the United States of America  Print Number: 01    Print Year: 2019 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Brief Contents ABOUT THE AUTHORS  xix PREFACE xxiii Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Data and Statistics  Descriptive Statistics: Tabular and Graphical Displays  33 Descriptive Statistics: Numerical Measures  107 Introduction to Probability  177 Discrete Probability Distributions  223 Continuous Probability Distributions  281 Sampling and Sampling Distributions  319 Interval Estimation  373 Hypothesis Tests  417 Inference About Means and Proportions with Two Populations  481 Inferences About Population Variances  525 Comparing Multiple Proportions, Test of Independence and Goodness of Fit  553 Experimental Design and Analysis of Variance  597 Simple Linear Regression  653 Multiple Regression  731 References and Bibliography  800 Tables 802 Summation Notation  829 Answers to Even-Numbered Exercises (MindTap Reader) Microsoft Excel 2016 and Tools for Statistical Analysis  831 Computing p-Values with JMP and Excel  839 Index 843 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Contents ABOUT THE AUTHORS  xix PREFACE xxiii Data and Statistics   Statistics in Practice: Bloomberg Businessweek  1.1    Applications in Business and Economics  Accounting 3 Finance 3 Marketing 4 Production 4 Economics 4 Information Systems  1.2   Data 5 Elements, Variables, and Observations  Scales of Measurement  Categorical and Quantitative Data  Cross-Sectional and Time Series Data  1.3   Data Sources 10 Existing Sources  10 Observational Study  11 Experiment 12 Time and Cost Issues  13 Data Acquisition Errors  13 1.4   Descriptive Statistics 13 1.5   Statistical Inference 15 1.6   Analytics 16 1.7    Big Data and Data Mining  17 1.8    Computers and Statistical Analysis  19 Chapter 1.9    Ethical Guidelines for Statistical Practice  19 Summary 21 Glossary 21 Supplementary Exercises  22 Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked form with JMP  30 Appendix 1.2 Getting Started with R and RStudio (MindTap Reader) Appendix 1.3 Basic Data Manipulation in R (MindTap Reader) Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Contents Descriptive Statistics: Tabular and Graphical Displays  33 Statistics in Practice: Colgate-Palmolive Company  34 2.1    Summarizing Data for a Categorical Variable  35 Frequency Distribution  35 Relative Frequency and Percent Frequency Distributions  36 Bar Charts and Pie Charts  37 2.2    Summarizing Data for a Quantitative Variable  42 Frequency Distribution  42 Relative Frequency and Percent Frequency Distributions  44 Dot Plot  45 Histogram 45 Cumulative Distributions  47 Stem-and-Leaf Display  47 2.3    Summarizing Data for Two Variables Using Tables  57 Crosstabulation 57 Simpson’s Paradox  59 2.4   Summarizing Data for Two Variables Using Graphical Displays 65 Scatter Diagram and Trendline  65 Side-by-Side and Stacked Bar Charts  66 2.5   Data Visualization: Best Practices in Creating Effective Graphical Displays  71 Creating Effective Graphical Displays  71 Choosing the Type of Graphical Display  72 Data Dashboards  73 Data Visualization in Practice: Cincinnati Zoo and Botanical Garden  75 Chapter Summary 77 Glossary 78 Key Formulas  79 Supplementary Exercises  80 Case Problem 1: Pelican Stores  85 Case Problem 2: Movie Theater Releases  86 Case Problem 3: Queen City  87 Case Problem 4: Cut-Rate Machining, Inc.  88 Appendix 2.1 Creating Tabular and Graphical Presentations with JMP 90 Appendix 2.2 Creating Tabular and Graphical Presentations with Excel  93 Appendix 2.3 Creating Tabular and Graphical Presentations with R (MindTap Reader) Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it v www.ebookslides.com vi Contents Descriptive Statistics: Numerical Measures   107 Statistics in Practice: Small Fry Design  108 3.1   Measures of Location  109 Mean 109 Weighted Mean  111 Median 112 Geometric Mean  113 Mode 115 Percentiles 115 Quartiles   116 3.2   Measures of Variability  122 Range 123 Interquartile Range  123 Variance 123 Standard Deviation  125 Coefficient of Variation  126 3.3   Measures of Distribution Shape, Relative Location, and Detecting Outliers  129 Distribution Shape  129 z-Scores 130 Chebyshev’s Theorem  131 Empirical Rule  132 Detecting Outliers  134 3.4   Five-Number Summaries and Boxplots  137 Five-Number Summary  138 Boxplot 138 Comparative Analysis Using Boxplots  139 3.5   Measures of Association Between Two Variables  142 Covariance 142 Interpretation of the Covariance  144 Correlation Coefficient  146 Interpretation of the Correlation Coefficient  147 Chapter 3.6   Data Dashboards: Adding Numerical Measures to Improve Effectiveness  150 Summary 153 Glossary 154 Key Formulas  155 Supplementary Exercises  156 Case Problem 1: Pelican Stores  162 Case Problem 2: Movie Theater Releases  163 Case Problem 3: Business Schools of Asia-Pacific  164 Case Problem 4: Heavenly Chocolates Website Transactions  164 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Contents Case Problem 5: African Elephant Populations  166 Appendix 3.1 Descriptive Statistics with JMP  168 Appendix 3.2 Descriptive Statistics with Excel  171 Appendix 3.3 Descriptive Statistics with R (MindTap Reader) Introduction to Probability   177 Statistics in Practice: National Aeronautics and Space Administration 178 4.1   Random Experiments, Counting Rules, and Assigning Probabilities  179 Counting Rules, Combinations, and Permutations  180 Assigning Probabilities  184 Probabilities for the KP&L Project  185 4.2    Events and Their Probabilities  189 4.3   Some Basic Relationships of Probability  193 Complement of an Event  193 Addition Law  194 4.4   Conditional Probability 199 Independent Events  202 Multiplication Law  202 4.5   Bayes’ Theorem 207 Tabular Approach  210 Summary 212 Glossary 213 Key Formulas  214 Supplementary Exercises  214 Case Problem 1: Hamilton County Judges  219 Case Problem 2: Rob’s Market  221 Chapter Discrete Probability Distributions   223 Statistics in Practice: Voter Waiting Times in Elections  224 5.1   Random Variables 225 Discrete Random Variables  225 Continuous Random Variables  225 5.2   Developing Discrete Probability Distributions  228 5.3   Expected Value and Variance  233 Expected Value  233 Variance 233 5.4   Bivariate Distributions, Covariance, and Financial Portfolios   238 A Bivariate Empirical Discrete Probability Distribution  238 Financial Applications  241 Summary 244 Chapter Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it vii www.ebookslides.com Appendix E Microsoft Excel 2016 and Tools for Statistical Analysis Figure E.6 Insert Function Dialog Box Figure E.7  escription of the Countif Function in the Insert Function D Dialog Box Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it 837 www.ebookslides.com 838 Appendix E Microsoft Excel 2016 and Tools for Statistical Analysis Figure E.8 Function Arguments Dialog Box for the Countif Function the COUNTIF function is shown in Figure E.8 This dialog box assists in creating the appropriate arguments (inputs) for the function selected When finished entering the arguments, we click OK; Excel then inserts the function into a worksheet cell Using Excel Add-Ins Excel’s Data Analysis Add-In Excel’s Data Analysis add-in, included with the basic Excel package, is a valuable tool for conducting statistical analysis Before you can use the Data Analysis add-in it must be ­installed To see if the Data Analysis add-in has already been installed, click the Data tab on the Ribbon In the Analyze group you should see the Data Analysis command If you not have an Analyze group and/or the Data Analysis command does not appear in the Analysis group, you will need to install the Data Analysis add-in The steps needed to install the Data Analysis add-in are as follows: Step 1.  Click the File tab Step 2.  Click Options Step 3.  When the Excel Options dialog box appears:         Select Add-Ins from the list of options (on the pane on the left)         In the Manage box, select Excel Add-Ins         Click Go Step 4.  When the Add-Ins dialog box appears:         Select Analysis ToolPak         Click OK Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Appendix F Computing p-Values Using JMP and Excel  Appendix F–Computing p-Values with JMP and Excel Here we describe how JMP and Excel can be used to compute p-values for the z, t, x2, and F statistics that are used in hypothesis tests As discussed in the text, only approximate p-­­values for the t, x2, and F statistics can be obtained by using tables This appendix is helpful to a person who has computed the test statistic by hand, or by other means, and wishes to use computer software to compute the exact p-value Computing p-values with JMP JMP can be used to provide the cumulative probability associated with the z, t, x2, and F test statistics The z test statistic  We use the Hilltop Coffee lower tail hypothesis test in Section 9.3 as an illustration; the value of the test statistic is z = −2.67 The JMP steps used to compute the cumulative probability corresponding to z = −2.67 follow Coffee Step 1. Select Help from the JMP ribbon Step 2. Choose Teaching Demos Step 3. Choose Distribution Calculator Step 4. When the Distribution Calculator dialog box appears: Select Normal from the drop down menu in the Distribution   Characteristics area Select Input values and calculate probability in the Type of Calculation   area In the Calculations area, select X , q and enter −2.67 in the Value box Press Enter on your keyboard Using these steps for the Hilltop Coffee lower tail test, JMP reports a p-value of 0038 in the Calculations area For an upper tail test, select X q and enter the value of z in the Value box For a two tail test, select X , q1 OR X q2, enter −|z| in the Value box, and enter |z| in the Value box The t test statistic  We use the Heathrow Airport example from Section 9.4 as an illustra- tion; the value of the test statistic is t = 1.84 with 59 degrees of freedom The JMP steps used to compute the p-value follow AirRating Step 1. Select Help from the JMP ribbon Step 2. Choose Teaching Demos Step 3. Choose Distribution Calculator Step 4.  When the Distribution Calculator dialog box appears: Select t from the drop down menu in the Distribution Characteristics area Enter 59 in the in the DF box in the Parameters section of the   Distribution Characteristics area Select Input values and calculate probability in the Type of Calculation   area In the Calculations area, select X q and enter 1.84 in the Value box Press Enter on your keyboard Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it 839 www.ebookslides.com 840 Appendix F Computing p-Values Using JMP and Excel Using these steps for the Heathrow Airport upper tail test, JMP reports a p-value of 0354 in the Calculations area For a lower upper tail test, select X , q and enter the value of t in the Value box For a two tail test, select X , q1 OR X q2, enter −|t| in the Value box, and enter |t| in the Value box The x 2 test statistic  We use the St Louis Metro Bus example from Section 11.1 as an illustration; the value of the test statistic is x2 = 28.18 with 23 degrees of freedom The JMP steps used to compute the p-value follow BusTimes Step 1. Select Help from the JMP ribbon Step Choose Teaching Demos Step Choose Distribution Calculator Step When the Distribution Calculator dialog box appears: Select Chi Square from the drop down menu in the Distribution   Characteristics area Enter 23 in the in the DF box in the Parameters section of the   Distribution Characteristics area Select Input values and calculate probability in the Type of Calculation   area In the Calculations area, select X q and enter 28.18 in the Value box Press Enter on your keyboard Using these steps for the St Louis Metro Bus example, JMP reports a p-value of 2091 in the Calculations area The F test statistic  We use the Dullus County Schools example from Section 11.2 as an illustration; the test statistic is F = 2.40 with 25 numerator degrees of freedom and 15 denominator degrees of freedom The JMP steps to compute the p-value follow SchoolBus Step 1. Select Help from the JMP ribbon Step 2.  Choose Teaching Demos Step 3.  Choose Distribution Calculator Step 4.  When the Distribution Calculator dialog box appears: Select F from the drop down menu in the Distribution Characteristics   area Enter 25 in the in the Numerator DF box and 15 in the Denominator DF   box in the Parameters section of the Distribution Characteristics area Select Input values and calculate probability in the Type of Calculation   area In the Calculations area, select X q and enter 2.40 in the Value box Press Enter on your keyboard Using these steps for the Dullus County Schools Bus example, JMP reports a p-value of 0406 in the Calculations area Because this is a two-tailed test, we double the p-value reported by JMP to obtain the p-value of 0812 for this hypothesis test Computing p-values with Excel p-Value Excel functions and formulas can be used to compute p-values associated with the z, t, x2, and F test statistics We provide a template in the data file entitled p-Value for use in computing these p-values Using the template, it is only necessary to enter the value of the test statistic and, if necessary, the appropriate degrees of freedom Refer to Figure F.1 as we Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Appendix F Computing p-Values Using JMP and Excel  841 describe how the template is used For users interested in the Excel functions and formulas being used, just click on the appropriate cell in the template The z test statistic  We use the Hilltop Coffee lower tail hypothesis test in Section 9.3 as an illustration; the value of the test statistic is z = −2.67 To use the p-value template for this hypothesis test, simply enter −2.67 into cell B6 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear For Hilltop Coffee, we would use the lower tail p-value = 0038 in cell B9 For an upper tail test, we would use the p-value in cell B10, and for a two-tailed test we would use the p-value in cell B11 The t Test Statistic We use the Heathrow Airport example from Section 9.4 as an illus- tration; the value of the test statistic is t = 1.84 with 59 degrees of freedom To use the p-value template for this hypothesis test, enter 1.84 into cell E6 and enter 59 into cell E7 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear The Heathrow Airport example involves an upper tail test, so we would use the upper tail p-value = 0354 provided in cell E10 for the hypothesis test The x 2 test statistic  We use the St Louis Metro Bus example from Section 11.1 as an illus- tration; the value of the test statistic is x2 = 28.18 with 23 degrees of freedom To use the p-value template for this hypothesis test, enter 28.18 into cell B18 and enter 23 into cell B19 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will ­appear The St Louis Metro Bus example involves an upper tail test, so we would use the upper tail p-value = 2091 provided in cell B23 for the hypothesis test FIGURE F.1 Excel Worksheet for Computing p-Values Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com 842 Appendix F Computing p-Values Using JMP and Excel The F test statistic We use the Dullus County Schools example from Section 11.2 as an illustration; the test statistic is F = 2.40 with 25 numerator degrees of freedom and 15 denominator degrees of freedom To use the p-value template for this hypothesis test, enter 2.40 into cell E18, enter 25 into cell E19, and enter 15 into cell E20 (see Figure F.1) After doing so, p-values for all three types of hypothesis tests will appear The Dullus County Schools example involves a two-tailed test, so we would use the two-tailed p-value = 0812 provided in cell E24 for the hypothesis test Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Index 843 Index Note: Chapter 22 can be found with the Online Content for this book Index entries found in these chapters are denoted by the chapter number (bolded), hyphen, and page number Page numbers followed by f indicate figures; n indicate footnotes; and t indicate tables A Accounting applications, Addition law for mutually exclusive events, 196 Adjusted multiple coefficient of determination, 744 African elephant populations, 166–167 Air Force training program, 546–547 Air traffic controller stress test, 621–624 Alliance Data Systems, 654 Alternative hypothesis (Ha) Chemitech assembly method experiment, 601 completely randomized design, 604 defined, 462 equality of population proportions, 555 forms, 421 hypothesis testing, 419–420 multinomial distribution goodness of fit test, 573 NCP observational study, 611 normal distribution goodness of fit test, 577 population mean (s known), 433t population mean (s unknown), 442t population proportion, 447t population variance, 534t population variances (two), 541t t test (simple linear regression), 677 test of independence, 565 Amazon, 18, 357 American Statistical Association, 19–21 Analysis of uncertainties, 178 Analysis of variance (ANOVA) assumptions, 601 between-treatments estimate, 602 conceptual overview, 601–603 Excel, 649–652 factorial experiment, 629 JMP, 646–649 overall sample mean, 602 randomized block design, 623 within-treatments estimate, 603 See also ANOVA table Analytics, 16–17 ANOVA See Analysis of variance (ANOVA) ANOVA table completely randomized design, 608t, 609t defined, 635 factorial experiment, 630t multiple regression, 749t randomized block design, 623t, 624t simple linear regression, 681t, 691f Application programming interface (API), 11 Approximate class width, 43 Area as measure of probability, 284–285 Arithmetic mean, 109 See also Mean Asia-Pacific business schools, 164, 165t Assigning probabilities basic requirements, 184 classical method, 184, 191 relative frequency method, 184–185 subjective method, 185 AT&T, 18 Audio data, 18 Automobile brand loyalty study, 556–562 Automobile value scores, 723–724, 792–793 B Bar chart, 37, 37f histogram, compared, 51 side-by-side, 66–67, 68f stacked, 67, 68f, 69 Basic requirements for assigning probabilities, 184 Bayes, Thomas, 209 Bayes’ theorem, 207–211 computing posterior probabilities of an event and its complement, 211 decision analysis, 211 defined, 213 formula, 210 probability revision, 207, 207f tabular approach, 213–214 two-event case, 209 Bayview University, 469–470 Beer preference and gender, 565–569 Bell curve, 287–289 Bernoulli, Jakob, 248 Beta, 666, 721 Between-treatments estimate, 602, 605–606 Biased estimator, 350, 350f Big data, 18, 356–358 confidence intervals, 398–400 defined, 361 four V’s, 356 hypothesis testing, 459–461 multiple regression, 782–783 sampling error, 357–358 simple linear regression, 710–711 sources, 356 tall data, 357 terminology for describing size of data sets, 357t wide data, 357 Bimodal, 115 Binomial experiment, 248 Binomial probability distribution, 247–255, 396, 447 Binomial probability function, 252 Binomial probability tables, 253–254 Bipartisan agenda for change, 587 Bivariate empirical discrete probability distribution, 238–239, 239t Bivariate probability, 239 Bivariate probability distribution, 238 Blocking, 621, 635 Bloomberg, 10 Bloomberg Businessweek, Boxplot, 138–140 Buckeye Creek Amusement Park, 724–725 Bureau of Labor Statistics, 11t Burke Marketing Services, Inc., 598 Butler trucking example, 735–738 C Car value scores, 723–724, 792–793 Case problems African elephant populations, 166–167 Air Force training program, 546–547 Asia-Pacific business schools, 164, 165t Bayview University, 469–470 bipartisan agenda for change, 587 Buckeye Creek Amusement Park, 724–725 car value scores, 723–724, 792–793 Consumer Research, Inc., 790–791 Cut-Rate Machining, Inc., 88–89 Fresno Board Games, 588–589 Fuentes Salty Snacks, Inc., 588 Gebhardt Electronics, 311 Go Bananas! breakfast cereal, 272 Gulf Real Estate Properties, 407–409 Hamilton County judges, 219–220 Heavenly Chocolates, 164–166 Marion Dairies, 366 McNeil’s Auto Mall, 272–273 Meticulous Drill & Reamer, 547–548 Metropolitan Research, Inc., 409 movie theater releases, 86–87, 163 Nascar drivers’ winnings, 791–792 Par, Inc., 514 Pelican Stores, 85–86, 162–163 point-and-shoot digital cameras, 722–723 Quality Associates, Inc., 467–468 Queen City, 87 Rob’s Market, 221 sales professionals’ compensation, 644 Specialty Toys, Inc., 309–310 stock market risk, 721 TourisTopia Travel, 644–645 Tuglar Corporation grievance committee, 273–274 U.S Department of Transportation, 721–722 Wentworth Medical Center, 643 Young Professional magazine, 406–407 Categorical data, 7, 35 Categorical independent variables, 755–761 Categorical variable, 7, 35–42 Cause-and-effect relationships, 599, 657, 681 Census, 15 Census Bureau, 11t Central limit theorem, 336, 336f, 341 Central tendency See Measures of location Chapter-opening examples See Statistics in Practice boxes Chebyshev’s theorem, 131–132, 135 Chemitech assembly method experiment, 599–603 Chi-square distribution, 527, 554 See also Chi-square test Chi-square distribution table, 529t, 559t Chi-square test automobile brand loyalty study, 556–562 beer preference and gender, 565–569 equality of population proportions, 555–562, 582 Excel, 593–595 goodness of fit test, 573–580, 582 JMP, 590–593 multinomial distribution goodness of fit test, 573–576 normal distribution goodness of fit test, 576–580 population variance, 527–534 test of independence, 565–569, 582 Cincinnati Zoo and Botanical Garden, 75–77 Class limits, 43–44, 51 Class midpoint, 44 Classes, 43–44 Classical method of assigning probabilities, 184, 191, 228 Cluster sampling, 352–353, 352f Coefficient of determination (r2), 668–672 Coefficient of variation, 126 Colgate-Palmolive Company, 34 Collectively exhaustive events, 210n Combination, 182–183 Comparative analysis using boxplots, 139–140, 175–176 Comparing multiple proportions, 553–595 See also Chi-square test Comparisonwise type I error rate, 617 Complement of A (Ac), 193 Complete block design, 625 Completely randomized design, 604–611 ANOVA table, 608t, 609t between-treatments estimate, 605–606 comparing variance estimates, 606–608 defined, 635 Excel, 649–650 F statistic, 607 JMP, 646 mean square due to error (MSE), 606, 612 mean square due to treatments (MSTR), 605, 612 null/alternative hypothesis, 604 observational study, 610–611 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com 844 Index Completely randomized design (continued ) overall sample mean, 605, 612 overview (Chemitech), 600f partitioning of sum of squares, 609, 637 rejection rule, 608 sample mean, 604, 636 sample variance, 604, 636 sum of squares due to treatments (SSTR), 605 sum of squares to error (SSE), 606 total sum of squares (SST), 609 within-treatments estimate, 606 Computer software, 19 See also Excel; JMP Conditional probability, 199–202 Confidence coefficient, 377 Confidence interval 95%, 377 big data, 398–400 defined, 402, 685 Fisher’s LSD procedure, 617 multiple regression, 754 simple linear regression, 679, 685–686, 688f Confidence level, 377 Conjoint analysis, 627 Consistency, 351 Consumer Research, Inc., 790–791 Continuity correction factor, 299 Continuous probability distributions, 281–317 continuity correction factor, 299 discrete distributions, contrasted, 283, 285 Excel, 317 exponential probability density function, 303 exponential probability distribution, 302–304 JMP, 312–316 normal approximation of binomial probabilities, 299–300 normal distribution See Normal probability distribution normal probability density function, 288 probability density function, 283 standard normal density function, 290 standard normal probability distribution, 289–294 uniform probability density function, 283 uniform probability distribution, 283–285 Continuous random variable, 225, 226t Convenience sampling, 353 Cook’s distance measure, 767–769 Correlation coefficient, 146–148 defined, 154 interpretation of, 147–148, 241, 244 negative linear relationship, 148, 241 positive linear relationship, 148, 241 random variables, 241, 244 sample, 146 simple linear regression, 671–672, 727 Counting rule combinations, 183, 187 multi-step experiments, 180 permutations, 183 Covariance, 142–146 defined, 155 interpretation of, 144–146 negative linear association, 144, 145f population, 144 positive linear association, 144, 145f random variables, 240 sample, 142–143 Coverage error, 355 Critical value, 429 Critical value approach one-tailed test—population mean (s known), 429, 433t one-tailed test—population mean (s unknown), 440, 442t population proportion, 446, 447t steps used in hypothesis testing, 434 two-tailed test—population mean (s known), 433, 433t two-tailed test—population mean (s unknown), 441, 442t Cross-sectional data, Crosstabulation, 57–59 Cumulative frequency distribution, 47, 47t, 51 Cumulative percent frequency distribution, 47, 47t Cumulative relative frequency distribution, 47, 47t Cut-Rate Machining, Inc., 88–89 D Data audio, 18 big See Big data categorical, 7, 35 cross-sectional, defined, quantitative, 7, 35 tall, 357 text, 18 time series, 8, 9f video, 18 wide, 357 Data acquisition errors, 13 Data dashboard, 73–75 defined, 73 example dashboard, 74f, 76f, 77f KPIs, 73 numerical measures, 150–153 DATA GOV, 11, 11t, 12f Data mining, 18 Data set, 5, 6–7t Data sources data acquisition errors, 13 existing sources, 10–11 experiment, 12 observational study, 11–12 time and cost issues, 13 Data visualization Cincinnati Zoo and Botanical Garden example, 75–77 computer software, 77 data dashboard See Data dashboard defined, 35 geographic information system (GIS), 77 Data warehousing, 18 data.ca.gov, 11 data.texas.gov, 11 de Moivre, Abraham, 287 Decile, 117 Degree of belief, 185 Degrees of freedom chi-square distribution, 558, 559t defined, 402 t distribution, 381 two independent random samples, 490 Department of Commerce, 11t Dependent variable, 655 Descriptive analytics, 17 Descriptive statistics defined, 13 numerical measures, 107–176 See also Numerical measures tabular and graphical displays, 33–106 See also Tabular and graphical displays Deviation about the mean, 123 Digital dashboard, 73 See also Data dashboard Discrete probability distributions, 223–279 binomial probability distribution, 247–255 binomial probability function, 252 bivariate probability distribution, 238 continuous distributions, contrasted, 283, 285 developing, 228–230 discrete uniform probability function, 229 empirical discrete distribution, 228 Excel, 278–279 expected value See Expected value financial portfolios, 241–244 hypergeometric probability distribution, 262–263 hypergeometric probability function, 262 JMP, 275–278 Poisson probability distribution, 258–260 Poisson probability function, 258 random variables, 225–226 required conditions for discrete probability function, 229 types, 224, 265 Discrete random variable, 225, 226t Discrete uniform probability function, 229 Distribution shape, 129–130 Doctrine of Chances, The (de Moivre), 287 Dot plot, 45, 45f Double-blind experimental design, 604 Dow Jones & Company, 10 Dow Jones Industrial Average Index, 8, 9f Dummy variable, 756 Dun & Bradstreet, 10 E Economic applications, eighty*84.51°, 732 Elections, voter waiting times, 224 Elements, Empirical discrete distribution, 228 Empirical rule, 132–134 Equality of k population means, 604 See also Completely randomized design Equality of population proportions, 555–562, 582 Estimated logistic regression equation, 773 Estimated logit, 778 Estimated multiple regression equation, 734, 753–754 Estimated regression line, 657 Estimated simple linear regression equation, 656, 660, 691f “Ethical Guidelines for Statistical Practice,” 19–20 Ethics, 19–21 Event collectively exhaustive, 210n complement, 193 defined, 189 independent, 202 intersection, 195 mutually exclusive, 196, 197f probability, 190 union, 194 Exabyte (EB), 357t Excel analysis of variance (ANOVA), 649–652 bar chart, 93–94 BINOM.DIST, 278, 279 boxplot, 173–175 chi-square test, 593–595 CHISQ.TEST, 593–595 comparative boxplot, 175–176 completely randomized design, 649–650 continuous probability distributions, 317 COUNTA, 416 covariance, 172 crosstabulation, 98–101 Data Analysis ToolPak, 173 descriptive statistics, 173 discrete probability distributions, 278–279 equality of population proportions, 593–595 EXPON.DIST, 317 factorial experiment, 651–652 frequency distribution (categorical data), 93–94 frequency distribution (quantitative data), 95–98 GEOMEAN, 114 goodness of fit test, 593–595 histogram, 95–98 hypothesis testing, 475–479 interval estimation, 413–416 mean, 171 median, 172 mode, 172 multiple regression, 797–798 NORM.DIST, 317 NORM.INV, 317 opening files, 93 PERCENTILE.EXC, 116 population variances, 551–552 POWER, 114 QUARTILE.DOC, 117 random sampling, 371–372 randomized block design, 650–651 scatter diagram, 101–103 side-by-side bar chart, 103–104 simple linear regression, 728–730 stacked bar chart, 104–106 standard deviation, 172 test of independence, 593–595 two populations, 519–523 variance, 172 Expected frequencies vs observed frequencies, 556 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Index Expected value b1, 678 binomial distribution, 254 defined, 233 discrete random variable, 233 hypergeometric distribution, 263, 267 linear combination of random variables, 242 Expected value of p  ¯, 344 Expected value of x  ¯, 334, 367 Experiment binomial, 248 defined, 187, 213 factorial See Factorial experiment multi-step, 180 random, 179 single-factor, 599 Experimental design, 597–652 completely randomized design See Completely randomized design data collection, 600–601 double-blind, 604 factorial experiment See Factorial experiment randomized block design See Randomized block design terminology, 599 Experimental outcomes defined, 180 number of experimental outcomes providing x successes n trials, 250 Experimental statistical study, 598 Experimental units, 599 Experimentwise type I error rate, 618 Exponential probability density function, 303 Exponential probability distribution, 302–304 computing probabilities, 302–303 cumulative probabilities, 303 mean/standard deviation are equal, 303 Poisson distribution, and, 303–304 skewness, 304 F F distribution, 537–542 F distribution table, 538t F statistic completely randomized design, 607 multiple regression, 748 randomized block design, 625 simple linear regression, 680 F test multiple regression, 747–750, 748 simple linear regression, 679–680 Factor, 599 Factorial experiment, 627–632 ANOVA procedure, 629 ANOVA table, 630t defined, 627 Excel, 651–652 interaction effect (factors A and B), 629 JMP, 647–649 main effect (factor A), 628, 629 main effect (factor B), 628, 629 sum of squares due to error (SSE), 630 sum of squares for factor A (SSA), 630 sum of squares for factor B (SSB), 630 sum of squares for interaction (SSAB), 630 total sum of squares (SST), 630 two-factor GMAT experiment, 627–632 FDA See Food and Drug Administration (FDA) Federal Reserve Board, 11t Federal Trade Commission (FTC), 425 Financial applications, 3–4 Financial portfolios, 241–244 Finite population correction factor, 334, 359, 461 Fisher, Ronald Aylmer, 599 Fisher’s LSD procedure, 615–617 Fitch Outlook, 5, 13 Fitch Rating, 5, Five-number summary, 138 Food and Drug Administration (FDA), 424, 482 Food Lion, 374 Frame, 321 Frequency distribution categorical variable, 35–36 quantitative variable, 42 Fresno Board Games, 588–589 Fuentes Salty Snacks, Inc., 588 G Galton, Francis, 655 GAO See U.S Government Accountability Office (GAO) Gauss, Carl Friedrich, 659 Gebhardt Electronics, 311 General Electric (GE), 18 Geographic information system (GIS), 77 Geometric mean, 113–115 Gigabyte (GB), 357t GIS See Geographic information system (GIS) GMAT two-factor factorial experiment, 627–632 Go Bananas! breakfast cereal, 272 “Goodness” of estimated regression equation, 668 Goodness of fit test defined, 573, 582 multinomial probability distribution, 573–576 normal probability distribution, 576–580 “Goodness” of sample, 354 Google, 11 Gosset, William Sealy, 381 Government agencies, 11, 11t Graduate Management Admission Council, 11 Graphical displays, 78f See also Tabular and graphical displays Growth factor, 113 Gulf Real Estate Properties, 407–409 H H0 See Null hypothesis (H0) Ha See Alternative hypothesis (Ha) Hadoop, 19 Hamilton County judges, 219–220 Heavenly Chocolates, 164–166 High leverage points, 706 Histogram, 45–47 845 bar chart, compared, 51 defined, 45, 79 example, 45f skewness, 46, 46f symmetric, 46, 46f uses, 46 Hypergeometric probability distribution, 262–263 Hypergeometric probability function, 262 Hypothesis testing, 417–479 alternative hypothesis, 419–420 See also Alternative hypothesis (Ha) big data, 459–461 decision making, 450 Excel, 475–479 interval estimation, compared, 434–435 JMP, 471–475 level of significance, 423 lot-acceptance hypothesis test, 450–453 MaxFlight hypothesis test, 430–433 multiple regression, 783 null hypothesis, 420–421 See also Null hypothesis (H0) operating characteristic curve, 453n population mean (s known), 425–433 population mean (s unknown), 439–442 population means (s1 and s2 known), 485–486 population means (s1 and s2 unknown), 491–493 population proportion, 445–447 population proportions (two), 505–506 population variance, 531–534 population variances (two), 537–542 power, 452 power curve, 453, 453f rejection rule See Rejection rule sample size, 455–458 steps in process, 434 type I error, 422–424 type II error, 422–424, 450–453 what is it?, 462 population means (matched samples), 497–499 population means (s1 and s2 known), 483–487 population means (s1 and s2 unknown), 489–493 population proportions, 503–506 Influential observations Cook’s distance measure, 767–769 defined, 784 multiple regression, 767–769 simple linear regression, 704–707 Information systems, Interaction, 629 Interaction effect (factors A and B), 629 Interquartile range (IQR), 123 Intersection of A and B, 195 Interval estimate, 374 See also Interval estimation Interval estimation, 373–416 big data and confidence intervals, 398–400 Excel, 413–416 general form of interval estimate, 374 hypothesis testing, compared, 434–435 JMP, 411–413 nonsampling error, 400 overview (population mean), 387f population mean (s known), 375–379 population mean (s unknown), 381–386 population means (s1 and s2 known), 483–485 population means (s1 and s2 unknown), 489–491 population proportion, 393–395 population proportions (two), 503–504 population variance, 527–531 purpose of interval estimate, 374 sample size, 386, 390–391, 400 simple linear regression, 685 Interval scale, IQR See Interquartile range (IQR) IRI, 10 ith residual, 668 I J Incomplete block design, 625 Independence of categorical variables (test of independence), 565–569 Independent events, 202 Independent simple random samples, 483 Independent variable, 655 Indicator variable, 756 Inferences about population variances, 525–552 chi-square distribution, 527–534 Excel, 551–552 F distribution, 537–542 JMP, 549–550 one population variance, 527–534 St Louis Bus Company example, 531–533 two population variances, 537–542 Inferences about two populations, 481–523 Excel, 519–523 JMP, 515–519 J.D Power and Associates, 555 JMP analysis of variance (ANOVA), 646–649 bar chart, 91–92 binomial probability, 275–277 box plot, 169, 170f chi-square test, 590–593 completely randomized design, 646 continuous probability distributions, 312–316 correlation, 170, 171f covariance, 170, 171f descriptive statistics, 168–169 discrete probability distributions, 275–278 equality of population proportions, 590–592 factorial experiment, 647–649 frequency distribution, 91–92 goodness of fit test, 592–593 histogram, 90 hypergeometric distribution, 278 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com 846 Index JMP (continued ) hypothesis testing, 471–475 interval estimation, 411–413 logistic regression, 796, 797f multiple regression, 794–796 opening DATA files, 30 Poisson distribution, 278 population variances, 549–550 random sampling, 368–371 randomized block design, 646–647 saving files as Excel or CSV files, 30 scatter diagram, 92, 93f simple linear regression, 727–728 stacked form (stacked data set), 31 stem-and-leaf display, 91 test of independence, 590–592 two populations, 515–519 Johnson Filtration example, 756–758 Joint probability, 200, 239 Judgment sampling, 354 K Key performance indicators (KPIs), 73, 150 Kilobyte (kB), 357t KPIs See Key performance indicators (KPIs) L Least squares criterion, 659, 734 Least squares method calculus-based derivation of least squares formula, 726 defined, 712, 784 multiple regression, 734–737 simple linear regression, 658–662 Level of significance, 423 Leverage, 764, 784 Leverage of observation, 699n, 707 Linear regression See Simple linear regression Logistic regression, 771–779 estimated logistic regression equation, 773 estimated logit, 778 JMP, 796, 797f logistic regression equation, 772 logit, 778 managerial use, 775–776 odds ratio, 776–778 testing for significance, 774–775 Logistic regression equation, 772 Logit, 778 Lot-acceptance hypothesis test, 450–453 Lower tail test p-value, 430 population mean (s known), 433t population mean (s unknown), 442t population proportion, 447t population variance, 534t See also One-tailed test LSD test, 615–617 M MAE See Mean absolute error (MAE) Main effect (factor A), 628, 629 Main effect (factor B), 628, 629 Manufacturing capacity utilization, Marascuilo procedure, 560 Margin of error defined, 402 interval estimate, 374 population mean (s known), 375–379 population mean (s unknown), 382–384 population proportion, 393, 396 Marginal probability, 200 Marion Dairies, 366 Marketing applications, Matched samples defined, 509 population means, 497–499 MaxFlight hypothesis test, 430–433 McDonald’s, 8, 9f, 325 McNeil’s Auto Mall, 272–273 MeadWestvaco Corporation, 320 Mean defined, 109 geometric, 113–115 normal distribution, 288 population, 111 sample, 109–110 trimmed, 117 weighted, 111–112 Mean absolute error (MAE), 126 Mean square, 748 Mean square due to treatments (MSTR), 605, 612 Mean square error (MSE) completely randomized design, 606, 612 simple linear regression, 677 Mean square regression (MSR) multiple regression, 748 simple linear regression, 680 Measurement error, 355 Measurement scales, 5–6 See also Scales of measurement Measures of association, 142–150 Measures of dispersion See Measures of variability Measures of location geometric mean, 113–115 mean, 109–111 See also Mean median, 112–113, 116 mode, 115 percentile, 115–116 quartile, 116–117 weighted mean, 111–112 Measures of variability, 122–129 coefficient of variation, 126 interquartile range (IQR), 123 mean absolute error (MAE), 126 range, 123 standard deviation, 125–126 variance, 123–125 Median, 112–113, 116 Megabyte (MB), 357t Meticulous Drill & Reamer, 547–548 Metropolitan Research, Inc., 409 Microsoft Excel See Excel Mode, 115 Movie theater releases, 86–87, 163 MSE See Mean square error (MSE) MSR See Mean square regression (MSR) MSTR See Mean square due to treatments (MSTR) Multi-step experiment, 180 Multicollinearity, 750–751 Multimodal, 115 Multinomial probability distribution, 562, 573, 583 Multinomial probability distribution goodness of fit test, 573–576 Multiple coefficient of determination, 743 Multiple coefficient of determination (R2), 743 Multiple comparison procedure, 560–562, 615–618 Multiple regression, 731–798 adjusted multiple coefficient of determination, 744 ANOVA table, 749t big data, 782–783 Butler trucking example, 735–738 categorical independent variables, 755–761 confidence interval, 754 Cook’s distance measure, 767–769 defined, 733, 784 dummy variable, 756 estimated multiple regression equation, 734, 753–754 estimation process, 734f Excel, 797–798 F test, 747–750 hypothesis testing, 783 influential observations, 767–769 JMP, 794–796 Johnson Filtration example, 756–758 least squares method, 734–737 logistic regression See Logistic regression mean square regression (MSR), 748 model assumptions, 746 multicollinearity, 750–751 multiple coefficient of determination, 743 multiple regression equation, 733 multiple regression model, 733 outliers, 766 prediction interval, 754 residual analysis, 764–769 response variable/response surface, 746 sample size, 782 Simmons Stores example, 771–779 studentized deleted residuals, 766 t test, 750 testing for significance, 747–751 total sum of squares (SST), 743 Multiple regression equation, 733 Multiple regression model, 733 Multiplication law, 202–203 Multiplication law for independent events, 203 Mutually exclusive events, 196, 197f N Nascar drivers’ winnings, 791–792 National Aeronautics and Space Administration (NASA), 178 NCP observational statistical study, 611 Negative linear association, 144, 145f Negative linear relationship, 656f Negative relationship, 66, 67f Nielsen Company, 4, 10 ninety*95% confidence interval, 377 No apparent relationship, 65–66, 67f Nominal scale, Nonprobability sampling techniques, 359, 461 Nonresponse error, 355 Nonsampling error, 355–356, 400, 461 Normal approximation of binomial probabilities, 299–300, 447 Normal curve, 287–289 Normal equation, 726 Normal probability density function, 288 Normal probability distribution, 287–296 Gear Tire Company example, 294–296 normal curve, 287–289 probabilities, 294 standard normal distribution, 289–294 standard normal random variable, 294 uses, 287 Normal probability distribution goodness of fit test, 576–580 Normal probability plot, 699–701 Normal scores, 700, 700t Null hypothesis (H0) Chemitech assembly method experiment, 601 completely randomized design, 604 defined, 463 equality of population proportions, 555, 582 forms, 421 hypothesis testing, 420–421 multinomial distribution goodness of fit test, 573 NCP observational study, 611 normal distribution goodness of fit test, 577 population mean (s known), 433t population mean (s unknown), 442t population proportion, 447t population variance, 534t population variances (two), 541t t test (simple linear regression), 677 test of independence, 565 Numerical measures, 107–176 boxplot, 138–140 Chebyshev’s theorem, 131–132, 135 correlation coefficient, 146–148 covariance, 142–146 data dashboard, 150–153 distribution shape, 129–130 empirical rule, 132–134 Excel, 171–176 five-number summary, 138 JMP, 168–171 measures of association, 142–150 measures of location, 109–122 See also Measures of location measures of variability, 122–129 See also Measures of variability outliers, 134–135, 138 z-score, 130–131, 134 O Observation, Observational study, 11–12, 598, 610–611 Observed frequencies vs expected frequencies, 556 Observed level of significance, 429 Odds in favor of an event occurring, 776 Odds ratio, 776–778 Office of Management and Budget, 11t Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Index One-tailed test defined, 463 forms, 425 population mean (s known), 425–430, 433t population mean (s unknown), 439–440, 442t See also Lower tail test; Upper tail test Operating characteristic curve, 453n Orange S.A., 18 Ordinal scale, 5–6 Outliers boxplots, 138 defined, 134 multiple regression, 766 simple linear regression, 703–704 standardized values (z-scores), 134–135 Overall sample mean, 602, 605, 612 P p-value, 427, 435, 463 p-value approach one-tailed test—population mean (s known), 427–429, 433t one-tailed test—population mean (s unknown), 440, 442t population proportion, 445–446, 447t small p-values, 435 steps used in hypothesis testing, 434 two-tailed test—population mean (s known), 432, 433t two-tailed test—population mean (s unknown), 441, 442t Pairwise comparison test, 560–562 Par, Inc., 514 Parameter, 322 Partitioning, 609, 637 Pearson, Karl, 655 Pearson product moment correlation coefficient population data, 146 sample data, 146 Pelican Stores, 85–86, 162–163 Percent frequency distribution, 36, 36t, 44, 44t Percentile, 115–116 Perfect negative linear relationship, 148 Perfect positive linear relationship, 148 Permutations, 183 Petabyte (PB), 357t Pie chart, 37, 38, 38f Point-and-shoot digital cameras, 722–723 Point estimate, 328 Point estimation, 327–329 Point estimator, 109, 328 biased, 350, 350f consistency, 351 defined, 361 efficiency, 350 unbiased, 349, 350, 350f Point-of-sale scanner data, 17 Poisson, Siméon, 258 Poisson probability distribution, 258–260 defined, 266 exponential distribution, and, 303–304 length or distance intervals, 260 mean/variance are equal, 260 Poisson probability function, 258 properties of Poisson experiment, 258 tables, 259t time intervals, 259–260 Poisson probability function, 258 Poisson probability tables, 259t Pooled estimator of p, 505 Pooled sample variance, 494 Pooled treatments estimate, 603 Population defined, 15 sampled, 321 target, 329 Population covariance, 144 Population mean, 111 Population mean (s known) hypothesis testing, 425–433 interval estimation, 375–379 one-tailed test, 425–430 two-tailed test, 430–433 Population mean (s unknown) hypothesis testing, 439–442 interval estimation, 381–386 one-tailed test, 439–440 two-tailed test, 440–441 Population parameter, 109, 153 Population proportion difference between two population proportions, 503–506 equality of population proportions, 555–562 hypothesis testing, 445–447 interval estimation, 393–395 Population standard deviation, 125 Population variance, 123–124 See also Inferences about population variances Positive linear association, 144, 145f Positive linear relationship, 656f Positive relationship, 65, 67f Posterior probabilities, 207 Power, 452 Power curve, 453, 453f Practical significance big data and confidence intervals, 400 big data and hypothesis testing, 461 defined, 402 multiple regression, 783 simple linear regression, 711 statistical significance, contrasted, 682 Prediction interval, 685, 686–688, 688f, 689, 754 Predictive analytics, 17 Prescriptive analytics, 17 Prior probability, 207 Probability, 177–221 addition law, 194–196 area, 284–285 assigning See Assigning probabilities Bayes’ theorem See Bayes’ theorem computing probability using the complement, 194 conditional, 199–202 defined, 178 events See Event joint, 200 marginal, 200 multiplication law, 202–203 posterior, 207 prior, 207 Probability density function, 283 Probability distribution continuous See Continuous probability distributions defined, 228 discrete See Discrete probability distributions Probability of an event, 190 Probability tree, 208, 208f Process variance, 527 Procter & Gamble (P&G), 282 Producer price index, Production applications, Professional integrity and accountability, 20 Protected LSD test, 617 pth percentile, 115, 116 Public opinion polls, 12 Python, 19 Q Quality Associates, Inc., 467–468 Quantitative data, 7, 35 Quantitative variable, 7, 42–57 Quartile, 116–117 Queen City, 87 Quintile, 117 R R, 19, 77 r2 See Coefficient of determination (r2) R2 See Multiple coefficient of determination (R2) Random experiment, 179 Random number table, 323t Random sample, 324 Random variable, 225–226 Randomization, 599 Randomized block design, 621–625 air traffic controller stress test, 621–624 ANOVA procedure, 623 ANOVA table, 623t, 624t blocking, 621, 635 complete/incomplete block design, 625 defined, 621 error degrees of freedom, 625 Excel, 650–651 F statistic, 625 JMP, 646–647 purpose, 621 sum of squares due to blocks (SSBL), 624, 637 sum of squares due to error (SSE), 624, 637 sum of squares due to treatments (SSTR), 624, 637 total sum of squares (SST), 624, 637 Range, 123 Ratio scale, Regression analysis linear See Simple linear regression multiple regression See Multiple regression use, 655 Rejection rule completely randomized design, 608 critical value approach, 429 equality of population proportions, 560 847 F test for significance in multiple regression, 748 F test for significance in simple linear regression, 680 Fisher’s LSD procedure, 615 multinomial distribution goodness of fit test, 576 normal distribution goodness of fit test, 580 p-value approach, 428 population mean (s known), 433t population mean (s unknown), 442t population proportion, 447t population variance, 534t population variances (two), 541t t test for significance in multiple regression, 750 t test for significance in simple linear regression, 679 test of independence, 569 test of significance using correlation, 726 Relative efficiency, 350 Relative frequency distribution, 36, 36t, 44, 44t Relative frequency method of assigning probabilities, 184–185, 228 Replication, 600, 628 “Researches on the Probability of Criminal and Civil Verdicts” (Poisson), 258 Residual analysis defined, 712 multiple regression, 764–769 outliers and influential observations, 703–707 simple linear regression, 694–707 validating model assumptions, 694–701 Residual for observation i, 694 Residual plot, 695–698, 696t, 697t, 698t Response surface, 746 Response variable, 599, 746 Restricted LSD test, 617 Return on equity (ROE), 11–12 Rob’s Market, 221 S Sales professionals’ compensation, 644 Sample, 15 Sample covariance, 142–143 Sample mean, 109–110 Sample point, 180 Sample size hypothesis test about population mean, 455–458 interval estimate of population mean, 390 interval estimate of population proportion, 394 interval estimation, 386, 390–391, 400 multiple regression, 782 one-tailed hypothesis test about population mean, 457 population mean (s known), 379 population mean (s unknown), 385 population proportion, 394–395 simple linear regression, 710 Sample space, 179 Sample standard deviation, 125 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com 848 Index Sample statistic, 109, 153, 327, 329 Sample survey, 15 Sample variance, 124–125, 126 Sampled population, 321 Sampling and sampling distributions, 319–372 central limit theorem, 336, 336f, 341 cluster sampling, 352–353, 352f convenience sampling, 353 Electronics Associates, Inc (EAI) sampling problem, 321–322 examples of how sampling used, 321 Excel, 371–372 ¯, 344 expected value of p  expected value of x  ¯, 334, 367 finite population, 322–323, 325 finite population correction factor, 334, 359 frame, 321 “goodness” of sample, 354 infinite population, 324–325 JMP, 368–371 judgment sampling, 354 nonprobability sampling techniques, 359 nonsampling error, 355–356 point estimation, 327–329 See also Point estimator random sample, 324 sample statistic, 327, 329 sampled population, 321 sampling distribution, 331 sampling distribution of p  ¯, 343–347 sampling distribution of x  ¯, 333–340 sampling error, 354 sampling with/without replacement, 323 simple random sample, 322 standard deviation of p  ¯, 344 standard deviation of x  ¯, 334, 335, 367–368 standard error, 335 stratified random sampling, 352, 352f systematic sampling, 353 target population, 329 Sampling distribution, 331 See also Sampling and sampling distributions Sampling distribution of p  ¯, 343–347 Sampling distribution of x  ¯, 333–340 Sampling error, 354 Sampling with replacement, 323 Sampling without replacement, 323 SAS, 19 SAS Visual Analytics, 77 SAT scores, Scales of measurement interval scale, nominal scale, ordinal scale, 5–6 ratio scale, Scatter diagram, 65–66, 66f, 67f simple linear regression, 658, 659f Side-by-side bar chart, 66–67, 68f s See Standard deviation (s) Significance tests, 423 See also Testing for significance Simmons Stores example, 771–779 Simple linear regression, 653–730 ANOVA table, 681t, 691f big data, 710–711 coefficient of determination, 668–672 confidence interval, 679, 685–686, 688f correlation coefficient, 671–672 defined, 655 dependent/independent variable, 655 estimated regression line, 657 estimated regression equation, 656, 660, 691 estimation process, 657f Excel, 728–730 F test, 679–680 F test statistic, 680 influential observations, 704–707 interval estimation, 685 JMP, 727–728 least squares method, 658–662, 726 mean square error (MSE), 677 mean square regression (MSR), 680 model assumptions, 675–676 normal probability plot, 699–701 outliers, 703–704 positive/negative linear relationship, 656f prediction interval, 686–688, 688f, 689 residual analysis (outliers and influential observations), 703–707 residual analysis (validating model assumptions), 694–701 residual plot, 695–698, 696t, 697t, 698t sample size, 710 simple linear regression equation, 656 simple linear regression model, 655 standard error of the estimate, 677 standardized residuals, 698–699 sum of squares due to error (SSE), 668, 670 sum of squares due to regression (SSR), 669, 670 t test for significance, 677–679 testing for significance, 676–682, 727 total sum of squares (SST), 669, 670 Simple linear regression equation, 656 Simple linear regression model, 655 Simple random sample, 322 Simpson’s paradox, 60–62 Single-factor experiment, 599 Skewness, 46, 46f, 129–130 Small Fry Design, 108 Software packages See Excel; JMP Spearman rank correlation coefficient, 149 Specialty Toys, Inc., 309–310 Spotfire, 77 SPSS, 19 SSA See Sum of squares for factor A (SSA) SSAB See Sum of squares for interaction (SSAB) SSB See Sum of squares for factor B (SSB) SSBL See Sum of squares due to blocks (SSBL) SSE See Sum of squares to error (SSE) SSR See Sum of squares due to regression (SSR) SST See Total sum of squares (SST) SSTR See Sum of squares due to treatments (SSTR) St Louis Bus Company example, 531–533 Stacked bar chart, 67, 68f, 69 Standard deviation (s), 125–126 b1, 678 defined, 125, 234 ith residual, 698, 764 measure of risk in investing in stock, 126 normal distribution, 288 population, 125 sample, 125 Standard deviation of p  ¯, 344 Standard deviation of x  ¯, 334, 335, 367–368 Standard error defined, 335 two population proportions, 503, 505 two populations, 484 Standard error of b1, 678 Standard error of the estimate, 677 Standard error of the proportion, 345 Standard normal density function, 290 Standard normal probability distribution, 289–294, 381f Standard normal probability table, 291 Standard normal random variable, 294 Standardized residual, 698–699, 764 Standardized value, 131 See also z-score Statistical inference, 15, 16f Statistical quality control charts, Statistical rating organizations, 5n Statistical significance, 682 Statistical software packages See Excel; JMP Statistics, applications in business and economics, 3–4 defined, 22 descriptive See Descriptive statistics ethical guidelines, 19–21 real-life examples See Statistics in Practice boxes Statistics in Practice boxes Alliance Data Systems, 654 Bloomberg Businessweek, Burke Marketing Services, Inc., 598 Colgate-Palmolive Company, 34 eighty*84.51°, 732 Food Lion, 374 MeadWestvaco Corporation, 320 National Aeronautics and Space Administration (NASA), 178 Procter & Gamble (P&G), 282 Small Fry Design, 108 United Way, 554 U.S Food and Drug Administration (FDA), 482 U.S Government Accountability Office (GAO), 526 voter waiting times, 224 Stem-and-leaf display, 47–50 Stock beta, 666, 721 Stock market risk, 721 Strata, 352 Stratified random sampling, 352, 352f “Student” (William Sealy Gosset), 381 Studentized deleted residuals, 766 Subjective method of assigning probabilities, 185, 228 Sum of squares due to blocks (SSBL), 624, 637 Sum of squares due to error (SSE) completely randomized design, 606 factorial experiment, 630 randomized block design, 624, 637 simple linear regression, 668, 670 Sum of squares due to regression (SSR), 669, 670 Sum of squares due to treatments (SSTR) completely randomized design, 605 randomized block design, 624, 637 Sum of squares for factor A (SSA), 630 Sum of squares for factor B (SSB), 630 Sum of squares for interaction (SSAB), 630 Surveys, 12 Symmetric distribution, 130 Symmetric histogram, 46, 46f Systematic sampling, 353 T t distribution defined, 402 degrees of freedom, 381 uses, 381 t distribution table, 383t, 678 t test multiple regression, 750 simple linear regression, 677–679 Tableau, 77 Tabular and graphical displays, 33–106 bar chart, 37, 37f categorical variable, 35–42 choosing type of graphical display, 72–73 creating effective graphical display, 72 crosstabulation, 57–59 cumulative frequency distribution, 47, 47t, 51 cumulative percent frequency distribution, 47, 47t cumulative relative frequency distribution, 47, 47t dot plot, 45, 45f Excel, 93–106 frequency distribution, 35–36, 42 graphical displays used to make comparisons, 73 graphical displays used to show distribution of data, 73 graphical displays used to show relationships, 73 histogram, 45–47 JMP, 90–93 overview, 78f percent frequency distribution, 36, 36t, 44, 44t pie chart, 37, 38, 38f quantitative variable, 42–57 relative frequency distribution, 36, 36t, 44, 44t scatter diagram, 65–66, 66f, 67f side-by-side bar chart, 66–67, 68f stacked bar chart, 67, 68f, 69 stem-and-leaf display, 47–50 Tabular approach to Bayes’ theorem calculations, 213–214 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Index Tall data, 357 Target population, 329 Terabyte (TB), 357t Test data set, 19 Test of independence, 565–569, 582 Test statistic defined, 463 equality of k population means, 607 equality of population proportions, 557, 558t goodness of fit, 574 population mean (s known), 426–427 population mean (s unknown), 439 population means (s1 and s2 known), 485 population means (s1 and s2 unknown), 491 population proportion, 446 population proportions (two), 505 population variance, 531 population variances (two), 539 Testing for significance correlation, 727 logistic regression, 774–775 multiple regression, 747–751 simple linear regression, 676–682 Text data, 18 Thearling, Kurt, 18 Three-dimensional pie chart, 38f Time series, 69 Time series data, 8, 9f Time series plot, 69 Tossing a coin, 180 Total sum of squares (SST) completely randomized design, 609 factorial experiment, 630 multiple regression, 743 randomized block design, 624, 637 simple linear regression, 669, 670 TourisTopia Travel, 644–645 Training data set, 19 Treatment, 599 Tree diagram, 180, 181f, 182f, 208f Trendline, 65, 66f Trimmed mean, 117 Tuglar Corporation grievance committee, 273–274 Twitter, 11 Two-factor GMAT experiment, 627–632 Two populations See Inferences about two populations Two-tailed test defined, 463 population mean (s known), 430–433, 433t population mean (s unknown), 440–441, 442t population proportion, 447t population variance, 534t population variances (two), 541t Type I error comparisonwise type I error rate, 617 defined, 463 experimentwise type I error rate, 618 hypothesis testing, 422–424 Type II error defined, 463 hypothesis testing, 422–424, 450–453 U Unemployment rate, Uniform probability density function, 283 Uniform probability distribution, 283–285 Union of A and B, 194 United Way, 554 Upper tail test p-value, 430 population mean (s known), 433t population mean (s unknown), 442t population proportion, 447t population variance, 534t population variances (two), 541t See also One-tailed test U.S Department of Transportation, 721–722 U.S Food and Drug Administration (FDA), 424, 482 U.S Golf Association (USGA), 430 U.S Government Accountability Office (GAO), 526 U.S Travel Association, 10 V Value Line, 721n Variable categorical, 7, 35–42 categorical independent, 755–761 defined, dependent, 655 dummy, 756 independent, 655 quantitative, 7, 42–57 random, 225–226 response, 599 Variance, 123–125 binomial distribution, 254 defined, 123, 233 discrete random variable, 234 849 hypergeometric distribution, 263 linear combination of random variables, 243 measured in squared units, 234 population, 123–124 sample, 124–125, 126 Variety, 356 Velocity, 356 Venn diagram, 193 Veracity, 356 Video data, 18 Volume, 356 Voter waiting times, 224 W Walmart, 18 Weighted mean, 111–112 Wentworth Medical Center, 643 Whiskers, 138 Wide data, 357 Williams, Walter, 424 Within-treatments estimate, 603, 606 World Trade Organization, Y Yahoo!, 11 Yottabyte (YB), 357t Young Professional magazine, 406–407 Z z-score, 130–131, 134 Zettabyte (ZB), 357t Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Statistics for Business and Economics 14e Revised DATAfiles Chapter Nations Table 1.1 Table 1.5 Rogers CompactSUV ­Exercise 25 Appendix 1.1 Nations Chemitech Appendix 1.1 Chapter SoftDrink Websites Names2018 Networks2012 AirSurvey BaseballHall Majors HotelRatings Audit AptitudeTest Frequency NBAPlayerPts Airports CEOTime Endowments Franchise MarketIndexes EngineeringSalary BestPayingDegrees Marathon Restaurant Crosstab Crosstab2 BrandValue FuelData2018 Electronics Scatter MPG Snow Hypertension Smartphones ManagerTime NewSAT MedianHousehold Population2012 StartUps BBB StocksBeta FortuneBest100 Colleges ElectricVehicles Zoo PelicanStores Movies2016 QueenCity CutRate Audit AptitudeTest Table 2.1 Exercise Exercise Exercise Exercise Exercise Exercise Exercise 10 Table 2.4 Table 2.8 Exercise 11 Exercise 18 Exercise 19 Exercise 20 Exercise 21 Exercise 22 Exercise 23 Exercise 24 Exercise 25 Exercise 26 Table 2.9 Exercise 27 Exercise 28 Table 2.12 Table 2.12 Table 2.12 Exercise 36 Exercise 39 Exercise 40 Exercise 41 Exercise 42 Exercise 43 Exercise 44 Exercise 45 Exercise 46 Exercise 47 Exercise 48 Table 2.17 Exercise 52 Exercise 54 Exercise 57 Exercise 58 Table 2.19 Table 2.20 Case Problem Case Problem Appendix 2.1 Appendix 2.1 SoftDrink Electronics SoftDrink Audit Restaurant Electronics Restaurant Restaurant Appendix 2.1 Appendix 2.1 Appendix 2.2 Appendix 2.2 Appendix 2.2 Appendix 2.2 Appendix 2.2 Appendix 2.2 Chapter StartingSalaries MutualFund eICU AdvertisingSpend JacketRatings TelevisionViewing OnlineGame UnemploymentRates SFGasPrices Flights VarattaSales CellPhoneExpenses Advertising BestCities NCAA iPads MajorSalaries Runners PharmacySales CellService AdmiredCompanies BorderCrossings Electronics StockComparison SmokeDetectors Russell BestPrivateColleges Coaches WaitTracking Sleep Smartphone Transportation FoodIndustry Travel NFLTeamValue SpringTraining PanamaRailroad PelicanStores Movies2016 AsiaMBA HeavenlyChocolates AfricanElephants StartingSalaries MajorSalaries Electronics StartingSalaries Table 3.1 Table 3.2 Exercise Exercise Exercise 10 Exercise 11 Exercise 12 Exercise 14 Exercise 26 Exercise 27 Exercise 28 Exercise 31 Exercise 32 Exercise 43 Exercise 44 Exercise 45 Figure 3.8 Exercise 50 Exercise 51 Exercise 52 Exercise 53 Exercise 54 Table 3.6 Exercise 57 Exercise 59 Exercise 60 Exercise 61 Exercise 63 Exercise 64 Exercise 65 Exercise 66 Exercise 67 Exercise 69 Exercise 70 Exercise 71 Exercise 72 Exercise 75 Table 3.9 Table 3.10 Case Problem Table 3.12 Table 3.13 Appendix 3.1 Appendix 3.1 Appendix 3.1 Appendix 3.2 Electronics StartingSalaries MajorSalaries Appendix 3.2 Appendix 3.2 Appendix 3.2 Chapter CodeChurn Judge MarketBasket Exercise 10 Table 4.8 Case Problem Chapter Coldstream12 Exercise 17 Chapter EAI Morningstar ShadowStocks USCitiesPop MetAreas Section 7.1 Exercise 14 Exercise 50 Appendix 7.2 Appendix 7.3 Chapter Lloyds Houston TravelTax Setters TobaccoFires NewBalance Scheer CorporateBonds Miami JobSearch HongKongMeals AutoInsurance TeleHealth Guardians TeeTimes RightDirection CasualDining FedTaxErrors FedSickHours SleepHabits DrugCost Obesity 35MPH UnderEmployed FloridaFraud Professional GulfProp Auto Lloyds NewBalance TeeTimes Lloyds NewBalance IntervalProp Section 8.1 Exercise Exercise Exercise Exercise Table 8.3 Table 8.3 Exercise 16 Exercise 17 Exercise 18 Exercise 19 Exercise 20 Exercise 21 Exercise 22 Section 8.4 Exercise 37 Exercise 38 Exercise 44 Exercise 45 Exercise 51 Exercise 52 Exercise 53 Exercise 65 Exercise 66 Exercise 67 Case Problem Table 8.9 Case Problem Appendix 8.1 Appendix 8.1 Appendix 8.1 Appendix 8.2 Appendix 8.2 Appendix 8.2 Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.ebookslides.com Chapter Coffee GolfTest Fowle AirRating Orders ResidentialWater ChildCare UsedCars WomenGolf HomeState Eagle LawSuit FedEmail SocialNetwork BritainMarriages WeeklyHSGradPay ChannelSurfing TSAWaitTimes Quality Bayview GolfTest AirRating WomenGolf HypSigmaKnown HypSigmaUnknown HypothesisProp Chapter 12 Section 9.3 Section 9.3 Exercise 21 Section 9.4 Section 9.4 Exercise 29 Exercise 30 Exercise 32 Section 9.5 Exercise 39 Exercise 43 Exercise 44 Exercise 60 Exercise 61 Exercise 68 Exercise 69 Exercise 81 Exercise 83 Case Problem Case Problem Appendix 9.1 Appendix 9.1 Appendix 9.1 Appendix 9.2 Appendix 9.2 Appendix 9.2 Chapter 10 ExamScores Hotel CheckAcct SoftwareTest CollegeCosts IntHotels SATMath LateFlights Matched StockQuarter BusinessTravel TestScores GolfScores TaxPrep SUVLease Mutual Twins ComputerNews Golf CheckAcct Matched TaxPrep ExamScores SoftwareTest Matched Section 10.1 Exercise Section 10.2 Table 10.1 Exercise 13 Exercise 15 Exercise 16 Exercise 18 Table 10.3 Exercise 22 Exercise 24 Exercise 25 Exercise 26 Section 10.4 Exercise 39 Exercise 40 Exercise 42 Exercise 46 Case Problem Appendix 10.1 Appendix 10.1 Appendix 10.1 Appendix 10.2 Appendix 10.2 Appendix 10.2 Chapter 11 BusTimes CoachSalary Halloween StockPriceChange Costco EconGMAT SchoolBus Bags BatteryLife Travel Training MeticulousDrills SchoolBus SchoolBus Section 11.1 Exercise Exercise Exercise Exercise 10 Exercise 11 Section 11.2 Exercise 19 Exercise 21 Exercise 25 Case Problem Case Problem Appendix 11.1 Appendix 11.2 AutoLoyalty Table 12.1 SocialMedia Exercise Table 12.6 BeerPreference WorkforcePlan Exercise 12 Exercise 13 Millenials AutoQuality Exercise 14 Section 12.3 Chemline M&M Exercise 22 Exercise 25 Temperatures Demand Exercise 26 Exercise 32 Ambulance Grades Exercise 36 Case Problem NYReform FuentesChips Case Problem Case Problem BBG AutoLoyalty Appendix 12.1 AutoLoyaltySummary Appendix 12.1 ScottMarketingSummary Appendix 12.1 Appendix 12.2 AutoLoyalty ChiSquare Appendix 12.2 Chapter 13 Chemitech NCP Exer6 AudJudg Paint GrandStrand Triple-A AirTraffic SATScores Airfares Toothpaste GMATStudy MobileApps HybridTest SatisJob OzoneLevels CollegeRates Assembly JobAutomation TalkShows ClubHead Medical1 Medical2 SalesSalary AirTraffic Chemitech GMATStudy Chemitech AirTraffic GMATStudy Table 13.1 Table 13.4 Exercise Exercise 10 Exercise 11 Exercise 12 Exercise 20 Table 13.5 Exercise 24 Exercise 25 Exercise 26 Table 13.10 Exercise 30 Exercise 32 Key Formulas Key Formulas Key Formulas Key Formulas Exercise 34 Exercise 36 Exercise 37 Exercise 40 Exercise 40 Exercise 42 Case Problem Appendix 13.1 Appendix 13.1 Appendix 13.2 Appendix 13.2 Appendix 13.2 Chapter 14 Armand’s Table 14.1,   Appendix 14.3, 14.4 NFLPassing Exercise Sales Exercises 7, 19, & 36 BrokerRatings Exercises & 28 Landscape Exercises WinePrices Exercises 10 Computer Exercise 11 CocaCola Exercise 12 RacingBicycles Exercises 20 & 31 GPASalary Exercises 27 BrokerRatings Exercises 28 RestaurantLine Exercises 35 BusinessTravel Exercises 39 Setup Exercises 43 RaceHelmets RentMortgage Charities Checkout MLBValues DJIAS&P500 WSHouses OnlineEdu Jensen Absent AgeCost HoursPts MktBeta IRSAudit Camry Beta Safety Cameras FamilySedans BuckeyeCreek Exercise 44 Exercise 49 Exercise 52 Exercise 53 Exercise 54 Exercise 58 Exercise 59 Exercise 60 Exercise 61 Exercise 63 Exercise 64 Exercise 65 Exercise 66 Exercise 67 Exercise 68 Case Problem Case Problem Table 14.13 Case Problem Case Problem Chapter 15 Butler Tables 15.1 & 15.2 Exer2 Exercise 2, 28 Showtime Exercises 5, 15, 29 & 41 PassingNFL Exercises & 16 MonitorRatings Exercise Exercise Ships SpringHouses Exercise 9, 17 Exercise 10, 18, 26 PitchingMLB NFL Exercise 24 Exercise 25 AutoResale NFL2011 Exercise 30 Exercise 31 AutoResale Johnson Table 15.6 Repair Exercise 35 Exercise 37 Refrigerators Stroke Exercise 38 Exercise 42 Auto2 LPGA2014 Exercise 43 Simmons Table 15.11 &   Exercise 44,   Appendix 15.2 Exercise 46 Bank Lakeland Exercise 47 TireRatings Exercise 48 Exercise 54 TireRack ZooSpend Exercise 55 Exercise 56 MutualFunds GiftCards Exercise 57 Consumer Case Problem Case Problem NASCAR CarValues Case Problem Butler Appendix 15.1, 15.3 Appendix E SoftDrink Table E.1 Appendix F Coffee AirRating BusTimes SchoolBus p-Value Appendix F Appendix F Appendix F Appendix F Appendix F Copyright 2020 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it ... iStock.com/alienforce; iStock.com/TommL Essentials of Statistics for  Business and Economics 9e James J Cochran Thomas A Williams David R Anderson University of Alabama Rochester Institute of Technology... Distribution of x 333 Expected Value of x 334 Standard Deviation of x 334 Form of the Sampling Distribution of x 335 Sampling Distribution of x for the EAI Problem  337 Practical Value of the Sampling... Authors David R Anderson.   David R Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati Born in Grand Forks, North Dakota,