DAVID M. MCEVOY, PHD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.

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• Preface xiii1 Types of Data 11.1 Categorical Data 21.2 Numerical Data 31.3 Level of Measurement 41.4 Cross-Sectional, Time-Series, and Panel Data 51.5 Summary 72 Populations and Samples 92.1 What is the Population of Interest? 102.2 How to Sample From a Population? 112.2.1 Simple Random Sampling 112.2.2 Stratified Sampling 142.2.3 Other Methods 152.3 Getting the Data 162.4 Summary 173 Descriptive Statistics 193.1 Measures of Central Tendency 203.1.1 The Mean 203.1.2 The Median 233.1.3 The Mode 243.2 Measures of Variability 243.2.1 Variance and Standard Deviation 243.3 The Shape 263.4 Summary 28Technical Appendix 294 Probability 314.1 Simple Probabilities 324.1.1 When to Add Probabilities Together 344.1.2 When to Find Intersections 364.2 Empirical Probabilities 374.3 Conditional Probabilities 394.4 Summary 41Technical Appendix 425 The Normal Distribution 435.1 The Bell Shape 435.2 The Empirical Rule 445.3 Standard Normal Distribution 465.3.1 Probabilities with Continuous Distributions 485.3.2 Verifying the Empirical Rule Using the z-table 485.4 Normal Approximations 485.4.1 Mean 495.4.2 Standard deviation 495.4.3 Shape 505.5 Summary 51Technical Appendix 526 Sampling Distributions 556.1 Defining a Sampling Distribution 556.2 The Importance of Sampling Distributions 566.3 An Example of a Sampling Distribution 576.4 Characteristics of a Sampling Distribution of a Mean 616.4.1 The Mean 616.4.2 The Shape 626.4.3 The Standard Deviation 646.4.4 Finding Probabilities With a Sampling Distribution 656.5 Sampling Distribution of a Proportion 676.5.1 The Mean 686.5.2 The Shape 686.5.3 The Standard Deviation 686.6 Summary 70Technical Appendix 717 Confidence Intervals 737.1 Confidence Intervals for Means 747.1.1 The Characteristics of the Sampling Distribution 757.1.2 Confidence Intervals Using the z-Distribution 767.1.3 Confidence Intervals Using the t-Distribution 787.2 Confidence Intervals for Proportions 807.3 Sample Size and the Width of Confidence Intervals 817.4 Comparing Two Proportions From the Same Poll 827.5 Summary 84Technical Appendix 858 Hypothesis Tests of a Population Mean 898.1 Two-Tail Hypothesis Test of a Mean 908.1.1 A Single Sample from a Population 908.1.2 Setting Up the Null and Alternative Hypothesis 928.1.3 Decisions and Errors 928.1.4 Rejection Regions and Conclusions 948.1.5 Changing the Level of Significance 958.2 One-Tail Hypothesis Test of a Mean 978.2.1 Setting Up the Null and Alternative Hypotheses 978.2.2 Rejection Regions and Conclusions 988.3 p-Value Approach to Hypothesis Tests 998.3.1 One-Tail Tests 998.3.2 Two-tail tests 1008.4 Summary 100Technical Appendix 1019 Hypothesis Tests of Categorical Data 1039.1 Two-Tail Hypothesis Test of a Proportion 1049.1.1 A Single Sample from a Population 1049.1.2 Rejection Regions and Conclusions 1069.2 One-Tail Hypothesis Test of a Proportion 1079.3 Using p-Values 1089.3.1 One-Tail Tests Using the p-Value 1089.3.2 Two-Tail Tests Using the p-Value 1089.4 Chi-Square Tests 1099.4.1 The Data in a Contingency Table 1099.4.2 Chi-Square Test of Goodness of Fit 1119.5 Summary 114Technical Appendix 11510 Hypothesis Tests Comparing Two Parameters 11710.1 The Approach in this Chapter 11810.2 Hypothesis Tests of Two Means 11810.2.1 The Null and Alternative Hypothesis 11810.2.2 t-Test Assuming Equal Variances 12110.2.3 t-Test Assuming Unequal Variances 12210.2.4 One-Tail Hypothesis Tests of Two Means 12410.2.5 A Note on Hypothesis Tests Using Paired Observations 12410.3 Hypothesis Tests of Two Variances 12610.4 Hypothesis Tests of Two Proportions 12810.5 Summary 130Technical Appendix 13111 Simple Linear Regression 13311.1 The Population Regression Model 13411.2 A Look at the Data 13511.3 Ordinary Least Squares (OLS) 13711.4 The Distribution of b0 and b1 13911.5 Tests of Significance 14011.6 Goodness of Fit 14211.7 Checking for Violations of the Assumptions 14311.7.1 The Normality Assumption 14311.7.2 The Constant Variance Assumption 14411.8 Summary 146Technical Appendix 14712 Multiple Regression 14912.1 Population Regression Model 14912.2 The Data 15012.3 Sample Regression Function 15112.4 Interpreting the Estimates 15212.4.1 Attendance 15312.4.2 SAT 15312.4.3 Hours Studying 15312.4.4 Logic Test 15312.4.5 Female 15312.4.6 Senior 15412.5 Prediction 15412.6 Tests of Significance 15412.6.1 Joint Hypothesis Test 15512.7 Goodness of Fit 15612.8 Multicollinearity 15712.8.1 Variance Inflation Factor (VIF) 15712.8.2 An Example of Violating the Assumption of no Multicollinearity 15912.9 Summary 162Technical Appendix 16313 More Topics in Regression 16513.1 Hypothesis Tests Comparing Two Means With Regression 16513.2 Hypothesis Tests Comparing More Than Two Means (ANOVA) 16813.3 Interacting Variables 17013.3.1 Gender Differences in Starting Wages 17113.3.2 Gender Differences in Wage Increase from Experience 17213.4 Nonlinearities 17313.5 Time-Series Analysis 17513.6 Summary 177Index 179

DAVID M. MCEVOY, PHD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.

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• Preface xiii1 Types of Data 11.1 Categorical Data 21.2 Numerical Data 31.3 Level of Measurement 41.4 Cross-Sectional, Time-Series, and Panel Data 51.5 Summary 72 Populations and Samples 92.1 What is the Population of Interest? 102.2 How to Sample From a Population? 112.2.1 Simple Random Sampling 112.2.2 Stratified Sampling 142.2.3 Other Methods 152.3 Getting the Data 162.4 Summary 173 Descriptive Statistics 193.1 Measures of Central Tendency 203.1.1 The Mean 203.1.2 The Median 233.1.3 The Mode 243.2 Measures of Variability 243.2.1 Variance and Standard Deviation 243.3 The Shape 263.4 Summary 28Technical Appendix 294 Probability 314.1 Simple Probabilities 324.1.1 When to Add Probabilities Together 344.1.2 When to Find Intersections 364.2 Empirical Probabilities 374.3 Conditional Probabilities 394.4 Summary 41Technical Appendix 425 The Normal Distribution 435.1 The Bell Shape 435.2 The Empirical Rule 445.3 Standard Normal Distribution 465.3.1 Probabilities with Continuous Distributions 485.3.2 Verifying the Empirical Rule Using the z-table 485.4 Normal Approximations 485.4.1 Mean 495.4.2 Standard deviation 495.4.3 Shape 505.5 Summary 51Technical Appendix 526 Sampling Distributions 556.1 Defining a Sampling Distribution 556.2 The Importance of Sampling Distributions 566.3 An Example of a Sampling Distribution 576.4 Characteristics of a Sampling Distribution of a Mean 616.4.1 The Mean 616.4.2 The Shape 626.4.3 The Standard Deviation 646.4.4 Finding Probabilities With a Sampling Distribution 656.5 Sampling Distribution of a Proportion 676.5.1 The Mean 686.5.2 The Shape 686.5.3 The Standard Deviation 686.6 Summary 70Technical Appendix 717 Confidence Intervals 737.1 Confidence Intervals for Means 747.1.1 The Characteristics of the Sampling Distribution 757.1.2 Confidence Intervals Using the z-Distribution 767.1.3 Confidence Intervals Using the t-Distribution 787.2 Confidence Intervals for Proportions 807.3 Sample Size and the Width of Confidence Intervals 817.4 Comparing Two Proportions From the Same Poll 827.5 Summary 84Technical Appendix 858 Hypothesis Tests of a Population Mean 898.1 Two-Tail Hypothesis Test of a Mean 908.1.1 A Single Sample from a Population 908.1.2 Setting Up the Null and Alternative Hypothesis 928.1.3 Decisions and Errors 928.1.4 Rejection Regions and Conclusions 948.1.5 Changing the Level of Significance 958.2 One-Tail Hypothesis Test of a Mean 978.2.1 Setting Up the Null and Alternative Hypotheses 978.2.2 Rejection Regions and Conclusions 988.3 p-Value Approach to Hypothesis Tests 998.3.1 One-Tail Tests 998.3.2 Two-tail tests 1008.4 Summary 100Technical Appendix 1019 Hypothesis Tests of Categorical Data 1039.1 Two-Tail Hypothesis Test of a Proportion 1049.1.1 A Single Sample from a Population 1049.1.2 Rejection Regions and Conclusions 1069.2 One-Tail Hypothesis Test of a Proportion 1079.3 Using p-Values 1089.3.1 One-Tail Tests Using the p-Value 1089.3.2 Two-Tail Tests Using the p-Value 1089.4 Chi-Square Tests 1099.4.1 The Data in a Contingency Table 1099.4.2 Chi-Square Test of Goodness of Fit 1119.5 Summary 114Technical Appendix 11510 Hypothesis Tests Comparing Two Parameters 11710.1 The Approach in this Chapter 11810.2 Hypothesis Tests of Two Means 11810.2.1 The Null and Alternative Hypothesis 11810.2.2 t-Test Assuming Equal Variances 12110.2.3 t-Test Assuming Unequal Variances 12210.2.4 One-Tail Hypothesis Tests of Two Means 12410.2.5 A Note on Hypothesis Tests Using Paired Observations 12410.3 Hypothesis Tests of Two Variances 12610.4 Hypothesis Tests of Two Proportions 12810.5 Summary 130Technical Appendix 13111 Simple Linear Regression 13311.1 The Population Regression Model 13411.2 A Look at the Data 13511.3 Ordinary Least Squares (OLS) 13711.4 The Distribution of b0 and b1 13911.5 Tests of Significance 14011.6 Goodness of Fit 14211.7 Checking for Violations of the Assumptions 14311.7.1 The Normality Assumption 14311.7.2 The Constant Variance Assumption 14411.8 Summary 146Technical Appendix 14712 Multiple Regression 14912.1 Population Regression Model 14912.2 The Data 15012.3 Sample Regression Function 15112.4 Interpreting the Estimates 15212.4.1 Attendance 15312.4.2 SAT 15312.4.3 Hours Studying 15312.4.4 Logic Test 15312.4.5 Female 15312.4.6 Senior 15412.5 Prediction 15412.6 Tests of Significance 15412.6.1 Joint Hypothesis Test 15512.7 Goodness of Fit 15612.8 Multicollinearity 15712.8.1 Variance Inflation Factor (VIF) 15712.8.2 An Example of Violating the Assumption of no Multicollinearity 15912.9 Summary 162Technical Appendix 16313 More Topics in Regression 16513.1 Hypothesis Tests Comparing Two Means With Regression 16513.2 Hypothesis Tests Comparing More Than Two Means (ANOVA) 16813.3 Interacting Variables 17013.3.1 Gender Differences in Starting Wages 17113.3.2 Gender Differences in Wage Increase from Experience 17213.4 Nonlinearities 17313.5 Time-Series Analysis 17513.6 Summary 177Index 179

DAVID M. MCEVOY, PHD, is an Associate Professor in the Economics Department at Appalachian State University in Boone NC. He has published over 20 peer-reviewed articles and is coeditor of two books. Dr. McEvoy is an award-winning educator who has taught undergraduate courses in business statistics for over 10 years.

Select Guide Rating

• Preface xiii1 Types of Data 11.1 Categorical Data 21.2 Numerical Data 31.3 Level of Measurement 41.4 Cross-Sectional, Time-Series, and Panel Data 51.5 Summary 72 Populations and Samples 92.1 What is the Population of Interest? 102.2 How to Sample From a Population? 112.2.1 Simple Random Sampling 112.2.2 Stratified Sampling 142.2.3 Other Methods 152.3 Getting the Data 162.4 Summary 173 Descriptive Statistics 193.1 Measures of Central Tendency 203.1.1 The Mean 203.1.2 The Median 233.1.3 The Mode 243.2 Measures of Variability 243.2.1 Variance and Standard Deviation 243.3 The Shape 263.4 Summary 28Technical Appendix 294 Probability 314.1 Simple Probabilities 324.1.1 When to Add Probabilities Together 344.1.2 When to Find Intersections 364.2 Empirical Probabilities 374.3 Conditional Probabilities 394.4 Summary 41Technical Appendix 425 The Normal Distribution 435.1 The Bell Shape 435.2 The Empirical Rule 445.3 Standard Normal Distribution 465.3.1 Probabilities with Continuous Distributions 485.3.2 Verifying the Empirical Rule Using the z-table 485.4 Normal Approximations 485.4.1 Mean 495.4.2 Standard deviation 495.4.3 Shape 505.5 Summary 51Technical Appendix 526 Sampling Distributions 556.1 Defining a Sampling Distribution 556.2 The Importance of Sampling Distributions 566.3 An Example of a Sampling Distribution 576.4 Characteristics of a Sampling Distribution of a Mean 616.4.1 The Mean 616.4.2 The Shape 626.4.3 The Standard Deviation 646.4.4 Finding Probabilities With a Sampling Distribution 656.5 Sampling Distribution of a Proportion 676.5.1 The Mean 686.5.2 The Shape 686.5.3 The Standard Deviation 686.6 Summary 70Technical Appendix 717 Confidence Intervals 737.1 Confidence Intervals for Means 747.1.1 The Characteristics of the Sampling Distribution 757.1.2 Confidence Intervals Using the z-Distribution 767.1.3 Confidence Intervals Using the t-Distribution 787.2 Confidence Intervals for Proportions 807.3 Sample Size and the Width of Confidence Intervals 817.4 Comparing Two Proportions From the Same Poll 827.5 Summary 84Technical Appendix 858 Hypothesis Tests of a Population Mean 898.1 Two-Tail Hypothesis Test of a Mean 908.1.1 A Single Sample from a Population 908.1.2 Setting Up the Null and Alternative Hypothesis 928.1.3 Decisions and Errors 928.1.4 Rejection Regions and Conclusions 948.1.5 Changing the Level of Significance 958.2 One-Tail Hypothesis Test of a Mean 978.2.1 Setting Up the Null and Alternative Hypotheses 978.2.2 Rejection Regions and Conclusions 988.3 p-Value Approach to Hypothesis Tests 998.3.1 One-Tail Tests 998.3.2 Two-tail tests 1008.4 Summary 100Technical Appendix 1019 Hypothesis Tests of Categorical Data 1039.1 Two-Tail Hypothesis Test of a Proportion 1049.1.1 A Single Sample from a Population 1049.1.2 Rejection Regions and Conclusions 1069.2 One-Tail Hypothesis Test of a Proportion 1079.3 Using p-Values 1089.3.1 One-Tail Tests Using the p-Value 1089.3.2 Two-Tail Tests Using the p-Value 1089.4 Chi-Square Tests 1099.4.1 The Data in a Contingency Table 1099.4.2 Chi-Square Test of Goodness of Fit 1119.5 Summary 114Technical Appendix 11510 Hypothesis Tests Comparing Two Parameters 11710.1 The Approach in this Chapter 11810.2 Hypothesis Tests of Two Means 11810.2.1 The Null and Alternative Hypothesis 11810.2.2 t-Test Assuming Equal Variances 12110.2.3 t-Test Assuming Unequal Variances 12210.2.4 One-Tail Hypothesis Tests of Two Means 12410.2.5 A Note on Hypothesis Tests Using Paired Observations 12410.3 Hypothesis Tests of Two Variances 12610.4 Hypothesis Tests of Two Proportions 12810.5 Summary 130Technical Appendix 13111 Simple Linear Regression 13311.1 The Population Regression Model 13411.2 A Look at the Data 13511.3 Ordinary Least Squares (OLS) 13711.4 The Distribution of b0 and b1 13911.5 Tests of Significance 14011.6 Goodness of Fit 14211.7 Checking for Violations of the Assumptions 14311.7.1 The Normality Assumption 14311.7.2 The Constant Variance Assumption 14411.8 Summary 146Technical Appendix 14712 Multiple Regression 14912.1 Population Regression Model 14912.2 The Data 15012.3 Sample Regression Function 15112.4 Interpreting the Estimates 15212.4.1 Attendance 15312.4.2 SAT 15312.4.3 Hours Studying 15312.4.4 Logic Test 15312.4.5 Female 15312.4.6 Senior 15412.5 Prediction 15412.6 Tests of Significance 15412.6.1 Joint Hypothesis Test 15512.7 Goodness of Fit 15612.8 Multicollinearity 15712.8.1 Variance Inflation Factor (VIF) 15712.8.2 An Example of Violating the Assumption of no Multicollinearity 15912.9 Summary 162Technical Appendix 16313 More Topics in Regression 16513.1 Hypothesis Tests Comparing Two Means With Regression 16513.2 Hypothesis Tests Comparing More Than Two Means (ANOVA) 16813.3 Interacting Variables 17013.3.1 Gender Differences in Starting Wages 17113.3.2 Gender Differences in Wage Increase from Experience 17213.4 Nonlinearities 17313.5 Time-Series Analysis 17513.6 Summary 177Index 179