Table of Contents:
1. Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 1.6 The Role of Statistics in Critical Thinking and Ethics 2. Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Measures of Central Tendency 2.4 Numerical Measures of Variability 2.5 Using the Mean and Standard Deviation to Describe Data 2.6 Numerical Measures of Relative Standing 2.7 Methods for Detecting Outliers: Box Plots and z-Scores 2.8 Graphing Bivariate Relationships (Optional) 2.9 Distorting the Truth with Descriptive Statistics 3. Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Some Additional Counting Rules (Optional) 3.8 Bayes's Rule (Optional) 4. Discrete Random Variables 4.1 Two Types of Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 Expected Values of Discrete Random Variables 4.4 The Binomial Random Variable 4.5 The Poisson Random Variable (Optional) 4.6 The Hypergeometric Random Variable (Optional) 5. Continuous Random Variables 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution 5.3 The Normal Distribution 5.4 Descriptive Methods for Assessing Normality 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 5.6 The Exponential Distribution (Optional) 6. Sampling Distributions 6.1 The Concept of a Sampling Distribution 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 6.3 The Sampling Distribution of (x-bar) and the Central Limit Theorem 6.4 The Sampling Distribution of the Sample Proportion 7. Inferences Based on a Single Sample: Estimation with Confidence Intervals 7.1 Identifying and Estimating the Target Parameter 7.2 Confidence Interval for a Population Mean: Normal (z) Statistic 7.3 Confidence Interval for a Population Mean: Student's t-Statistic 7.4 Large-Sample Confidence Interval for a Population Proportion 7.5 Determining the Sample Size 7.6 Confidence Interval for a Population Variance (Optional) 8. Inferences Based on a Single Sample: Tests of Hypothesis 8.1 The Elements of a Test of Hypothesis 8.2 Formulating Hypotheses and Setting Up the Rejection Region 8.3 Observed Significance Levels: p-Values 8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic 8.5 Test of Hypothesis about a Population Mean: Student's t-Statistic 8.6 Large-Sample Test of Hypothesis about a Population Proportion 8.7 Calculating Type II Error Probabilities: More about β (Optional) 8.8 Test of Hypothesis about a Population Variance (Optional) 9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 9.1 Identifying the Target Parameter 9.2 Comparing Two Population Means: Independent Sampling 9.3 Comparing Two Population Means: Paired Difference Experiments 9.4 Comparing Two Population Proportions: Independent Sampling 9.5 Determining the Sample Size 9.6 Comparing Two Population Variances: Independent Sampling (Optional) 10. Analysis of Variance: Comparing More than Two Means 10.1 Elements of a Designed Study 10.2 The Completely Randomized Design: Single Factor 10.3 Multiple Comparisons of Means 10.4 The Randomized Block Design 10.5 Factorial Experiments: Two Factors 11. Simple Linear Regression 11.1 Probabilistic Models 11.2 Fitting the Model: The Least Squares Approach 11.3 Model Assumptions 11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1 11.5 The Coefficients of Correlation and Determination 11.6 Using the Model for Estimation and Prediction 11.7 A Complete Example 12. Multiple Regression and Model Building 12.1 Multiple-Regression Models PART I: First-Order Models with Quantitative Independent Variables 12.2 Estimating and Making Inferences about the β Parameters 12.3 Evaluating Overall Model Utility 12.4 Using the Model for Estimation and Prediction PART II: Model Building in Multiple Regression 12.5 Interaction Models 12.6 Quadratic and Other Higher Order Models 12.7 Qualitative (Dummy) Variable Models 12.8 Models with Both Quantitative and Qualitative Variables (Optional) 12.9 Comparing Nested Models (Optional) 12.10 Stepwise Regression (Optional) PART III: Multiple Regression Diagnostics 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 13. Categorical Data Analysis 13.1 Categorical Data and the Multinomial Experiment 13.2 Testing Categorical Probabilities: One-Way Table 13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table 13.4 A Word of Caution about Chi-Square Tests 14. Nonparametric Statistics (available online) 14.1 Introduction: Distribution-Free Tests 14.2 Single-Population Inferences 14.3 Comparing Two Populations: Independent Samples 14.4 Comparing Two Populations: Paired Difference Experiment 14.5 Comparing Three or More Populations: Completely Randomized Design 14.6 Comparing Three or More Populations: Randomized Block Design 14.7 Rank Correlation APPENDICES A. Summation Notation B. Tables C. Calculation Formulas for Analysis of Variance Short Answers to Selected Odd-Numbered Exercises Index