1. Variation.

1.1 Variation.

1.2. Collecting Data.

1.3. Summarizing Your Data.

1.4. Types of Data.

1.5. Reporting Your Results.

1.6. Measures of Location.

1.7. Samples and Populations.

1.8. Variation— Within and Between.

1.9. Summary and Review.

2. Probability.

2.1. Probability.

2.2. Binomial.

2.3. Condition Probability.

2.4. Independence.

2.5. Applications to Genetics.

2.6. Summary and Review.

3. Distributions.

3.1. Distribution of Values.

3.2. Discrete Distributions.

3.3. Continuous Distributions.

3.4. Properties of Independence Observations.

3.5. Testing A Hypothesis.

3.6. Estimating Effect Size.

3.7 Summary and Review.

4. Testing Hypotheses.

4.1. One-Sample Problems.

4.2. Comparing Two Samples.

4.3. Which Test Should e Use?

4.4. Summary and Review.

5. Designing an Experiment or Survey.

5.1. The Hawthorne Effect.

5.2. Designing an Experiment or Survey.

5.3. How Large a Sample.

5.4. Meta-Analysis.

5.5. Summary and Review.

6. Analyzing Complex Experiments.

6.1. Changes Measured in Percentages.

6.2. Comparing More Than Two Samples.

6.3. Equalizing Variances.

6.4. Categorical Data.

6.5. Multivariate Analysis.

6.6. Summary and Review.

7. Developing Models.

7.1. Models.

7.2. Regression.

7.3. Fitting a Regression Equation.

7.4. Problems with Regression.

7.5 Quantile Regression.

7.6. Validation.

7.7 Classification and Regression Trees.

7.8 Summary and Review.

8. Reporting Your Findings.

8.1. What to Report.

8.2. Text, Tables, of Graph?

8.3. Summarizing Your Results.

8.4 Reporting Analysis Results.

8.5 Exceptions are the Real Story.

9. Problem Solving.

9.1. Real Life Problems.

9.2. Problem Sets.

9.3. Solutions.

Appendix: S-PLUS.

Answers to Selected Exercises.

Subject Index.

Index to R Functions.