Ch 1. Data Collection
1.1 Introduction to the Practice of Statistics
1.2 Observational Studies versus Designed Experiments
1.3 Simple Random Sampling
1.4 Other Effective Sampling Methods
1.5 Bias in Sampling
1.6 The Design of Experiments

Ch 2. Organizing and Summarizing Data
2.1 Organizing Qualitative Data
2.2 Organizing Quantitative Data: The Popular Displays
2.3 Additional Displays of Quantitative Data
2.4 Graphical Misrepresentations of Data

Ch 3. Numerically Summarizing Data
3.1 Measures of Central Tendency
3.2 Measures of Dispersion
3.3 Measures of Central Tendency and Dispersion from Grouped Data
3.4 Measures of Position and Outliers
3.5 The Five-Number Summary and Boxplots

Ch 4. Describing the Relation between Two Variables
4.1 Scatter Diagrams and Correlation
4.2 Least-Squares Regression
4.3 Diagnostics on the Least-Squares Regression Line
4.4 Contingency Tables and Association
4.5 Nonlinear Regression:Transformations (on CD)

Ch 5. Probability
5.1 Probability Rules
5.2 The Addition Rule and Complements
5.3 Independence and the Multiplication Rule
5.4 Conditional Probability and the General Multiplication Rule
5.5 Counting Techniques
5.6 Putting It Together: Which Method Do I Use?
5.7 Bayes's Rule (on CD)

Ch 6. Discrete Probability Distributions
6.1 Discrete Random Variables
6.2 The Binomial Probability Distribution
6.3 The Poisson Probability Distribution
6.4 The Hypergeometric Probability Distribution (on CD)

Ch 7. The Normal Probability Distribution
7.1 Properties of the Normal Distribution
7.2 The Standard Normal Distribution
7.3 Applications of the Normal Distribution
7.4 Assessing Normality
7.5 The Normal Approximation to the Binomial Probability Distribution

Ch 8. Sampling Distributions
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Ch 9. Estimating the Value of a Parameter Using Confidence Intervals
9.1 The Logic in Constructing Confidence Intervals for a Population Mean When the Population Standar
9.2 Confidence Intervals for a Population Mean When the Population Standard Deviation Is Unknown
9.3 Confidence Intervals for a Population Proportion
9.4 Confidence Intervals for a Population Standard Deviation
9.5 Putting It Together: Which Procedure Do I Use?

Ch 10. Hypothesis Tests Regarding a Parameter
10.1 The Language of Hypothesis Testing
10.2 Hypothesis Tests for a Population Mean—Population Standard Deviation Known
10.3 Hypothesis Tests for a Population Mean—Population Standard Deviation Unknown
10.4 Hypothesis Tests for a Population Proportion
10.5 Hypothesis Tests for a Population Standard Deviation
10.6 Putting It Together: Which Method Do I Use?
10.7 The Probability of a Type II Error and the Power of the Test

Ch 11. Inferences on Two Samples
11.1 Inference about Two Means: Dependent Samples
11.2 Inference about Two Means: Independent Samples
11.3 Inference about Two Population Proportions
11.4 Inference for Two Population Standard Deviations
11.5 Putting It Together: Which Method Do I Use?

Ch 12. Inference on Categorical Data
12.1 Goodness-of-Fit Test
12.2 Tests for Independence and the Homogeneity of Proportions

Ch 13. Comparing Three or More Means
13.1 Comparing Three or More Means (One-Way Analysis of Variance)
13.2 Post Hoc Tests on One-Way Analysis of Variance
13.3 The Randomized Complete Block Design
13.4 Two-Way Analysis of Variance

Ch 14. Inference on the Least-Squares Regression Model and Multiple Regression
14.1 Testing the Significance of the Least-Squares Regression Model
14.2 Confidence and Prediction Intervals
14.3 Multiple Regression

Ch 15. Nonparametric Statistics
15.1 An Overview of Nonparametric Statistics
15.2 Runs Test for Randomness
15.3 Inferences about Measures of Central Tendency
15.4 Inferences about the Difference between Two Medians: Dependent Samples
15.5 Inferences about the Difference between Two Medians: Independent Samples
15.6 Spearman's Rank-Correlation Test
15.7 Kruskal–Wallis Test