This book teaches statistics with a modern, data-analytic approach that uses graphing calculators and statistical software. It allows more emphasis to be put on statistical concepts and data analysis than on following recipes for calculations. This gives readers a more realistic understanding of both the theoretical and practical applications of statistics, giving them the ability to master the subject.
1 Statistical Reasoning: Investigating a Claim of Discrimination.1.1 Discrimination in the Workplace: Data Exploration.
1.2 Discrimination in the Workplace: Inference.
Chapter Summary.
2 Exploring Distributions of Data.
2.1 Visualizing Distributions: Shape, Center, and Spread.
2.2 Summarizing Center and Spread.
2.3 Working with Summary Statistics.
2.4 The Normal Distribution.
Chapter Summary.
3 Relationship between Two Quantitative Variables.
3.1 Scatterplots.
3.2 Regression: Getting a Line on the Pattern.
3.3 Correlation: The Strength of a Linear Trend.
3.4 Diagnostics: Looking for Features That the Summaries Miss.
Chapter Summary.
4 Sample Surveys and Experiments.
4.1 Random Sampling: Playing It Safe by Taking Chances.
4.2 Why Take Samples, and How Not To.
4.3 Experiments and Inference about Cause.
4.4 Designing Experiments to Reduce Variability.
Chapter Summary.
5 Probability Models.
5.1 Models of Random Behavior.
5.2 The Addition Rule and Disjoint Events.
5.3 Conditional Probability and the Multiplication Rule.
5.4 Independent Events.
Chapter Summary.
6 Probability Distributions.
6.1 Probability Distributions and Expected Value.
6.2 Rules for Means and Variances of Probability Distributions.
6.3 The Binomial Distribution.
Chapter Summary.
7 Sampling Distributions.
7.1 Generating Sampling Distributions.
7.2 Sampling Distribution of the Sample Mean.
7.3 Sampling Distribution of the Sample Proportion.
Chapter Summary.
8 Inference for a Proportion.
8.1 A Confidence Interval for a Proportion.
8.2 A Significance Test for a Proportion: Interpreting a P-Value.
8.3 A Significance Test for a Proportion: Making a Decision.
8.4 Types of Errors and Power of a Test.
Chapter Summary.
9 Comparing Two Populations: Inference for the Difference of Two Proportions.
9.1 A Confidence Interval for the Difference of Two Proportions.
9.2 A Significance Test for the Difference of Two Proportions.
9.3 Inference for Experiments and Observational Studies.
Chapter Summary.
10 Inference for Means.
10.1 A Confidence Interval for a Mean.
10.2 A Significance Test for a Mean: Interpreting a P-Value.
10.3 Fixed-Level Tests.
Chapter Summary.
11 Comparing Two Populations: Inference for the Difference of Two Means.
11.1 A Confidence Interval for the Difference of Two Means.
11.2 A Significance Test for the Difference of Two Means.
11.3 Inference for Paired Comparisons.
Chapter Summary.
12 Chi-Square Tests.
12.1 Testing a Probability Model: The Chi-Square Goodness-of-Fit Test.
12.2 The Chi-Square Test of Homogeneity.
12.3 The Chi-Square Test of Independence.
Chapter Summary.
13 Inference for Regression.
13.1 Variation in the Slope from Sample to Sample.
13.2 Making Inferences about Slopes.
Chapter Summary.
14 One- Way Analysis of Variance.
14.1 A New Look at the Two-Sample t-Test.
14.2 One-Way ANOVA: When There Are More Than Two Groups.
Chapter Summary.
15 Multiple Regression.
15.1 From One to Two Explanatory Variables.
15.2 From Two to More Explanatory Variables, including Categorical
Variables.
Chapter Summary.
16 Martin vs. Westvaco Revisited: Testing for Possible Discrimination in the Workplace.
Table A: Standard Normal Probabilities.
Table B: t-Distribution Critical Values.
Table C: x2 Critical Values.
Table D: F-Distribution Critical Values for α=0.05.
Table E: Random Digits.
Glossary.
Brief Answers to Practice Problems and Selected Exercises.