IFC-Abbreviated.qxd 11/22/10 7:03 PM Page 2 nshipMore Analyze Relatioamong Two or Variables Multiple regressionChapter 17 Not covered Not covered y c n e Analyze Relationshipbetween Two Variables Scatter diagramSection 3.3 CovarianceSection 4.4 Coefficient of correlation Section 4.4 Coefficient of determinationSection 4.4 Least squares lineSection 4.4 Simple linear regression andcorrelationChapter 16 Chi-squared test of a contingtableSection 15.2 e UES Compare Two orMore Populations One-way analysis of varianceSection 14.1 LSD multiple comparisonmethodSection 14.2 Tukey’s multiple comparisonmethodSection 14.2 Two-way analysis of varianceSection 14.4 Two-factor analysis of variancSection 14.5 Chi-squared test of acontingency tableSection 15.2 Q I N O STATISTICALTECH Compare Two Populations Equal-variances -test and estimatortof the difference between twomeans: independent samplesSection 13.1 Unequal-variances -test andtestimator of the difference betweentwo means: independent samplesSection 13.1 -test and estimator of meantdifference Section 13.3 -test and estimator of ratio of twoFvariancesSection 13.4 -test and estimator of thezdifference between two proportionsSection 13.5 Chi-squared test of a contingencytableSection 15.2 T E es A GUID Problem Objectiv Describe aPopulation HistogramSection 3.1 OgiveSection 3.1 Stem-and-leafSection 3.1 Line chartSection 3.2 Mean, median, and modeSection 4.1 Range, variance, andstandard deviation Section 4.2ion 4.2 Percentiles and quartilesSection 4.3 Box plotSection 4.3 -testandestimatorofameantSection 12.1 Chi-squared test andestimator of a varianceSection 12.2 Frequency distributionSection 2.2 Bar chartSection 2.2 Pie chartSection 2.2 -test and estimator zof a proportionSection 12.3 Chi-squared goodness-of-fit testSection 15.1 MedianSection 4.1 Percentiles and quartilesSection 4.3 Box plotSection 4.3 lavretnI lanimoN lanidrO SEPYT ATAD Copyright 2011 Cengage Learning. 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IFC-Abbreviated.qxd 11/22/10 7:03 PM Page 3 AMERICAN NATIONAL ELECTION SURVEY AND GENERAL SOCIAL SURVEY EXERCISES Chapter ANES Page GSS Page 2 2.34–2.37 31 3 3.62–3.67 82 3.25–3.28 64 3.68–3.71 82 4 4.37–4.38 117 4.39 117 4.58–4.60 126 4.61–4.62 126 4.86 144 4.84–4.85 144 12 12.51–12.53 413 12.46–12.50 413 12.116–12.123 435 12.103–12.115 434 13 13.42–13.44 472 13.38–13.41 472 13.73 488 13.70–13.72 488 13.123–13.125 512 13.113–13.122 511 A13.27–A13.30 524 A13.18–A13.126 523 14 14.27–14.32 543 14.21–14.26 542 14.47–14.50 553 14.43–14.46 552 14.66–14.67 563 A14.23–A14.25 594 A14.19–A14.22 594 15 15.17 604 15.18–15.21 604 15.43–15.46 615 15.39–15.42 614 A15.25–A15.28 631 A15.17–A15.24 630 16 16.45–16.49 665 16.50–16.53 666 16.73–16.76 671 16.77–16.80 671 A16.27–A16.28 689 A16.17–A16.26 689 17 17.21–17.22 713 17.16–17.20 712 A17.27–A17.28 734 A17.17–A17.26 733 APPLICATION SECTIONS Section4.5 (Optional)ApplicationinProfessionalSportsManagement:DeterminantsoftheNumber ofWinsinaBaseballSeason(illustratinganapplicationoftheleastsquaresmethodand correlation) 144 Section 4.6 (Optional) Application in Finance: Market Model (illustrating using a least squares lines and coefficient of determination to estimate a stock’s market-related risk and its firm-specific risk) 147 Section 7.3 (Optional) Application in Finance: Portfolio Diversification and Asset Allocation (illustrating the laws of expected value and variance and covariance) 237 Section 12.4 (Optional) Application in Marketing: Market Segmentation (using inference about a proportion to estimate the size of a market segment) 435 Section 14.6 (Optional) Application in Operations Management: Finding and Reducing Variation (using analysis of variance to actively experiment to find sources of variation) 578 APPLICATION SUBSECTION Section 6.4 (Optional) Application in Medicine and Medical Insurance: Medical Screening (using Bayes’s Law to calculate probabilities after a screening test) 199 Copyright 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. This page intentionally left blank Abb_FM.qxd 11/23/10 12:33 AM Page i Statistics FOR MANAGEMENT AND ECONOMICS ABBREVIATED 9e Copyright 2011 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. 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Abb_FM.qxd 11/23/10 12:33 AM Page vii B r i e f CO N T E N T S 1 What is Statistics? 1 2 Graphical Descriptive Techniques I 11 3 Graphical Descriptive Techniques II 43 4 Numerical Descriptive Techniques 97 5 Data Collection and Sampling 161 6 Probability 175 7 Random Variables and Discrete Probability Distributions 217 8 Continuous Probability Distributions 263 9 Sampling Distributions 307 10 Introduction to Estimation 335 11 Introduction to Hypothesis Testing 360 12 Inference about a Population 398 13 Inference about Comparing Two Populations 448 14 Analysis of Variance 525 15 Chi-Squared Tests 596 16 Simple Linear Regression and Correlation 633 17 Multiple Regression 692 A Appendix Data File Sample Statistics A-1 B Appendix Tables B-1 C Appendix Answers to Selected Even-Numbered Exercises C-1 Index I-1 vii Copyright 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. 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Abb_FM.qxd 11/23/10 12:33 AM Page viii CO N T E N T S 1 What Is Statistics? 1 Introduction 1 1.1 Key Statistical Concepts 5 1.2 Statistical Applications in Business 6 1.3 Large Real Data Sets 6 1.4 Statistics and the Computer 7 2 Graphical Descriptive Techniques I 11 Introduction 12 2.1 Types of Data and Information 13 2.2 Describing a Set of Nominal Data 18 2.3 Describing the Relationship between Two Nominal Variables and Comparing Two or More Nominal Data Sets 32 3 Graphical Descriptive Techniques II 43 Introduction 44 3.1 Graphical Techniques to Describe a Set of Interval Data 44 3.2 Describing Time-Series Data 64 3.3 Describing the Relationship between Two Interval Variables 74 3.4 Art and Science of Graphical Presentations 82 4 Numerical Descriptive Techniques 97 Introduction 98 Sample Statistic or Population Parameter 98 4.1 Measures of Central Location 98 4.2 Measures of Variability 108 4.3 Measures of Relative Standing and Box Plots 117 4.4 Measures of Linear Relationship 126 4.5 (Optional) Applications in Professional Sports: Baseball 144 viii Copyright 2011 Cengage Learning. 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