Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. Mr. Bruce is the coauthor of Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner®, Second Edition, also published by Wiley.

“The book is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.”  (Zentralblatt MATH, 1 April 2015)

• Preface ixAcknowledgments xiIntroduction xiii1 Designing and Carrying Out a Statistical Study 11.1 A Small Example 31.2 Is Chance Responsible? The Foundation of Hypothesis Testing 31.3 A Major Example 71.4 Designing an Experiment 81.5 What to Measure—Central Location 131.6 What to Measure—Variability 161.7 What to Measure—Distance (Nearness) 191.8 Test Statistic 211.9 The Data 221.10 Variables and Their Flavors 281.11 Examining and Displaying the Data 311.12 Are we Sure we Made a Difference? 39Appendix: Historical Note 391.13 Exercises 402 Statistical Inference 452.1 Repeating the Experiment 462.2 How Many Reshuffles? 482.3 How Odd is Odd? 532.4 Statistical and Practical Significance 552.5 When to use Hypothesis Tests 562.6 Exercises 563 Displaying and Exploring Data 593.1 Bar Charts 593.2 Pie Charts 613.3 Misuse of Graphs 623.4 Indexing 643.5 Exercises 684 Probability 714.1 Mendel’s Peas 724.2 Simple Probability 734.3 Random Variables and their Probability Distributions 774.4 The Normal Distribution 804.5 Exercises 845 Relationship between Two Categorical Variables 875.1 Two-Way Tables 875.2 Comparing Proportions 905.3 More Probability 925.4 From Conditional Probabilities to Bayesian Estimates 955.5 Independence 975.6 Exploratory Data Analysis (EDA) 995.7 Exercises 1006 Surveys and Sampling 1046.1 Simple Random Samples 1056.2 Margin of Error: Sampling Distribution for a Proportion 1096.3 Sampling Distribution for a Mean 1116.4 A Shortcut—the Bootstrap 1136.5 Beyond Simple Random Sampling 1176.6 Absolute Versus Relative Sample Size 1206.7 Exercises 1207 Confidence Intervals 1247.1 Point Estimates 1247.2 Interval Estimates (Confidence Intervals) 1257.3 Confidence Interval for a Mean 1267.4 Formula-Based Counterparts to the Bootstrap 1267.5 Standard Error 1327.6 Confidence Intervals for a Single Proportion 1337.7 Confidence Interval for a Difference in Means 1367.8 Confidence Interval for a Difference in Proportions 1397.9 Recapping 140Appendix A: More on the Bootstrap 141Resampling Procedure—Parametric Bootstrap 141Formulas and the Parametric Bootstrap 144Appendix B: Alternative Populations 144Appendix C: Binomial Formula Procedure 1447.10 Exercises 1478 Hypothesis Tests 1518.1 Review of Terminology 1518.2 A–B Tests: The Two Sample Comparison 1548.3 Comparing Two Means 1568.4 Comparing Two Proportions 1578.5 Formula-Based Alternative—t-Test for Means 1598.6 The Null and Alternative Hypotheses 1608.7 Paired Comparisons 163Appendix A: Confidence Intervals Versus Hypothesis Tests 167Confidence Interval 168Relationship Between the Hypothesis Test and the Confidence Interval 169Comment 170Appendix B: Formula-Based Variations of Two-Sample Tests 170Z-Test With Known Population Variance 170Pooled Versus Separate Variances 171Formula-Based Alternative: Z-Test for Proportions 1728.8 Exercises 1729 Hypothesis Testing—2 1789.1 A Single Proportion 1789.2 A Single Mean 1809.3 More Than Two Categories or Samples 1819.4 Continuous Data 1879.5 Goodness-of-Fit 187Appendix: Normal Approximation; Hypothesis Test of a Single Proportion 190Confidence Interval for a Mean 1909.6 Exercises 19110 Correlation 19310.1 Example: Delta Wire 19410.2 Example: Cotton Dust and Lung Disease 19510.3 The Vector Product and Sum Test 19610.4 Correlation Coefficient 19910.5 Other Forms of Association 20410.6 Correlation is not Causation 20510.7 Exercises 20611 Regression 20911.1 Finding the Regression Line by Eye 21011.2 Finding the Regression Line by Minimizing Residuals 21211.3 Linear Relationships 21311.4 Inference for Regression 21711.5 Exercises 22112 Analysis of Variance—ANOVA 22412.1 Comparing More Than Two Groups: ANOVA 22512.2 The Problem of Multiple Inference 22812.3 A Single Test 22912.4 Components of Variance 23012.5 Two-Way ANOVA 24012.6 Factorial Design 24612.7 Exercises 24813 Multiple Regression 25113.1 Regression as Explanation 25213.2 Simple Linear Regression—Explore the Data First 25313.3 More Independent Variables 25713.4 Model Assessment and Inference 26113.5 Assumptions 26713.6 Interaction Again 27013.7 Regression for Prediction 27213.8 Exercises 277Index 283

Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. Mr. Bruce is the coauthor of Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner®, Second Edition, also published by Wiley.

“The book is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.”  (Zentralblatt MATH, 1 April 2015)

• Preface ixAcknowledgments xiIntroduction xiii1 Designing and Carrying Out a Statistical Study 11.1 A Small Example 31.2 Is Chance Responsible? The Foundation of Hypothesis Testing 31.3 A Major Example 71.4 Designing an Experiment 81.5 What to Measure—Central Location 131.6 What to Measure—Variability 161.7 What to Measure—Distance (Nearness) 191.8 Test Statistic 211.9 The Data 221.10 Variables and Their Flavors 281.11 Examining and Displaying the Data 311.12 Are we Sure we Made a Difference? 39Appendix: Historical Note 391.13 Exercises 402 Statistical Inference 452.1 Repeating the Experiment 462.2 How Many Reshuffles? 482.3 How Odd is Odd? 532.4 Statistical and Practical Significance 552.5 When to use Hypothesis Tests 562.6 Exercises 563 Displaying and Exploring Data 593.1 Bar Charts 593.2 Pie Charts 613.3 Misuse of Graphs 623.4 Indexing 643.5 Exercises 684 Probability 714.1 Mendel’s Peas 724.2 Simple Probability 734.3 Random Variables and their Probability Distributions 774.4 The Normal Distribution 804.5 Exercises 845 Relationship between Two Categorical Variables 875.1 Two-Way Tables 875.2 Comparing Proportions 905.3 More Probability 925.4 From Conditional Probabilities to Bayesian Estimates 955.5 Independence 975.6 Exploratory Data Analysis (EDA) 995.7 Exercises 1006 Surveys and Sampling 1046.1 Simple Random Samples 1056.2 Margin of Error: Sampling Distribution for a Proportion 1096.3 Sampling Distribution for a Mean 1116.4 A Shortcut—the Bootstrap 1136.5 Beyond Simple Random Sampling 1176.6 Absolute Versus Relative Sample Size 1206.7 Exercises 1207 Confidence Intervals 1247.1 Point Estimates 1247.2 Interval Estimates (Confidence Intervals) 1257.3 Confidence Interval for a Mean 1267.4 Formula-Based Counterparts to the Bootstrap 1267.5 Standard Error 1327.6 Confidence Intervals for a Single Proportion 1337.7 Confidence Interval for a Difference in Means 1367.8 Confidence Interval for a Difference in Proportions 1397.9 Recapping 140Appendix A: More on the Bootstrap 141Resampling Procedure—Parametric Bootstrap 141Formulas and the Parametric Bootstrap 144Appendix B: Alternative Populations 144Appendix C: Binomial Formula Procedure 1447.10 Exercises 1478 Hypothesis Tests 1518.1 Review of Terminology 1518.2 A–B Tests: The Two Sample Comparison 1548.3 Comparing Two Means 1568.4 Comparing Two Proportions 1578.5 Formula-Based Alternative—t-Test for Means 1598.6 The Null and Alternative Hypotheses 1608.7 Paired Comparisons 163Appendix A: Confidence Intervals Versus Hypothesis Tests 167Confidence Interval 168Relationship Between the Hypothesis Test and the Confidence Interval 169Comment 170Appendix B: Formula-Based Variations of Two-Sample Tests 170Z-Test With Known Population Variance 170Pooled Versus Separate Variances 171Formula-Based Alternative: Z-Test for Proportions 1728.8 Exercises 1729 Hypothesis Testing—2 1789.1 A Single Proportion 1789.2 A Single Mean 1809.3 More Than Two Categories or Samples 1819.4 Continuous Data 1879.5 Goodness-of-Fit 187Appendix: Normal Approximation; Hypothesis Test of a Single Proportion 190Confidence Interval for a Mean 1909.6 Exercises 19110 Correlation 19310.1 Example: Delta Wire 19410.2 Example: Cotton Dust and Lung Disease 19510.3 The Vector Product and Sum Test 19610.4 Correlation Coefficient 19910.5 Other Forms of Association 20410.6 Correlation is not Causation 20510.7 Exercises 20611 Regression 20911.1 Finding the Regression Line by Eye 21011.2 Finding the Regression Line by Minimizing Residuals 21211.3 Linear Relationships 21311.4 Inference for Regression 21711.5 Exercises 22112 Analysis of Variance—ANOVA 22412.1 Comparing More Than Two Groups: ANOVA 22512.2 The Problem of Multiple Inference 22812.3 A Single Test 22912.4 Components of Variance 23012.5 Two-Way ANOVA 24012.6 Factorial Design 24612.7 Exercises 24813 Multiple Regression 25113.1 Regression as Explanation 25213.2 Simple Linear Regression—Explore the Data First 25313.3 More Independent Variables 25713.4 Model Assessment and Inference 26113.5 Assumptions 26713.6 Interaction Again 27013.7 Regression for Prediction 27213.8 Exercises 277Index 283

Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. Mr. Bruce is the coauthor of Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner®, Second Edition, also published by Wiley.

“The book is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.”  (Zentralblatt MATH, 1 April 2015)

• Preface ixAcknowledgments xiIntroduction xiii1 Designing and Carrying Out a Statistical Study 11.1 A Small Example 31.2 Is Chance Responsible? The Foundation of Hypothesis Testing 31.3 A Major Example 71.4 Designing an Experiment 81.5 What to Measure—Central Location 131.6 What to Measure—Variability 161.7 What to Measure—Distance (Nearness) 191.8 Test Statistic 211.9 The Data 221.10 Variables and Their Flavors 281.11 Examining and Displaying the Data 311.12 Are we Sure we Made a Difference? 39Appendix: Historical Note 391.13 Exercises 402 Statistical Inference 452.1 Repeating the Experiment 462.2 How Many Reshuffles? 482.3 How Odd is Odd? 532.4 Statistical and Practical Significance 552.5 When to use Hypothesis Tests 562.6 Exercises 563 Displaying and Exploring Data 593.1 Bar Charts 593.2 Pie Charts 613.3 Misuse of Graphs 623.4 Indexing 643.5 Exercises 684 Probability 714.1 Mendel’s Peas 724.2 Simple Probability 734.3 Random Variables and their Probability Distributions 774.4 The Normal Distribution 804.5 Exercises 845 Relationship between Two Categorical Variables 875.1 Two-Way Tables 875.2 Comparing Proportions 905.3 More Probability 925.4 From Conditional Probabilities to Bayesian Estimates 955.5 Independence 975.6 Exploratory Data Analysis (EDA) 995.7 Exercises 1006 Surveys and Sampling 1046.1 Simple Random Samples 1056.2 Margin of Error: Sampling Distribution for a Proportion 1096.3 Sampling Distribution for a Mean 1116.4 A Shortcut—the Bootstrap 1136.5 Beyond Simple Random Sampling 1176.6 Absolute Versus Relative Sample Size 1206.7 Exercises 1207 Confidence Intervals 1247.1 Point Estimates 1247.2 Interval Estimates (Confidence Intervals) 1257.3 Confidence Interval for a Mean 1267.4 Formula-Based Counterparts to the Bootstrap 1267.5 Standard Error 1327.6 Confidence Intervals for a Single Proportion 1337.7 Confidence Interval for a Difference in Means 1367.8 Confidence Interval for a Difference in Proportions 1397.9 Recapping 140Appendix A: More on the Bootstrap 141Resampling Procedure—Parametric Bootstrap 141Formulas and the Parametric Bootstrap 144Appendix B: Alternative Populations 144Appendix C: Binomial Formula Procedure 1447.10 Exercises 1478 Hypothesis Tests 1518.1 Review of Terminology 1518.2 A–B Tests: The Two Sample Comparison 1548.3 Comparing Two Means 1568.4 Comparing Two Proportions 1578.5 Formula-Based Alternative—t-Test for Means 1598.6 The Null and Alternative Hypotheses 1608.7 Paired Comparisons 163Appendix A: Confidence Intervals Versus Hypothesis Tests 167Confidence Interval 168Relationship Between the Hypothesis Test and the Confidence Interval 169Comment 170Appendix B: Formula-Based Variations of Two-Sample Tests 170Z-Test With Known Population Variance 170Pooled Versus Separate Variances 171Formula-Based Alternative: Z-Test for Proportions 1728.8 Exercises 1729 Hypothesis Testing—2 1789.1 A Single Proportion 1789.2 A Single Mean 1809.3 More Than Two Categories or Samples 1819.4 Continuous Data 1879.5 Goodness-of-Fit 187Appendix: Normal Approximation; Hypothesis Test of a Single Proportion 190Confidence Interval for a Mean 1909.6 Exercises 19110 Correlation 19310.1 Example: Delta Wire 19410.2 Example: Cotton Dust and Lung Disease 19510.3 The Vector Product and Sum Test 19610.4 Correlation Coefficient 19910.5 Other Forms of Association 20410.6 Correlation is not Causation 20510.7 Exercises 20611 Regression 20911.1 Finding the Regression Line by Eye 21011.2 Finding the Regression Line by Minimizing Residuals 21211.3 Linear Relationships 21311.4 Inference for Regression 21711.5 Exercises 22112 Analysis of Variance—ANOVA 22412.1 Comparing More Than Two Groups: ANOVA 22512.2 The Problem of Multiple Inference 22812.3 A Single Test 22912.4 Components of Variance 23012.5 Two-Way ANOVA 24012.6 Factorial Design 24612.7 Exercises 24813 Multiple Regression 25113.1 Regression as Explanation 25213.2 Simple Linear Regression—Explore the Data First 25313.3 More Independent Variables 25713.4 Model Assessment and Inference 26113.5 Assumptions 26713.6 Interaction Again 27013.7 Regression for Prediction 27213.8 Exercises 277Index 283