Statistics, Updated Edition, 13th edition

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