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For algebra-based Introductory Statistics courses.
Offering the most accessible approach with a strong visual/graphical emphasis, the text offers a vast number of examples on the premise that students learn best by doing. The Third Edition features many updates and revisions that place increased emphasis on interpretation of results and critical thinking over calculations.
(NOTE: Each chapter begins with Where You've Been and Where Your Going sections and concludes with Uses and Abuses, Chapter Summary, Review Exercises, Chapter Quiz, Real Statistics-Real Decisions-Putting it all Together, and Technology sections.)
1. Introduction to Statistics.
An Overview of Statistics. Data Classification. Case Study: Rating Television Shows in the United States. Experimental Design.
2. Descriptive Statistics.
Frequency Distributions and Their Graphs. More Graphs and Displays. Measures of Central Tendency. Measures of Variation. Case Study: Sunglass Sales in the United States. Measures of Position.
3. Probability.
Basic Concepts of Probability. Conditional Probability and the Multiplication Rule. The Addition Rule. Case Study: Probability and Parking Lot Strategies. Counting Principles.
4. Discrete Probability Distributions.
Probability Distributions. Binomial Distributions. Case Study: Binomial Distribution of Airplane Accidents. More Discrete Probability Distributions.
5. Normal Probability Distributions.
Introduction to Normal Distributions and the Standard Normal Distribution. Normal Distributions: Finding Probabilities. Normal Distributions: Finding Values.
Case Study: Birth Weights in America. The Central Limit Theorem. Normal Approximations to Binomial Distributions.
6. Confidence Intervals.
Confidence Intervals for the Mean (Large Samples). Case Study: Shell Lengths of Loggerhead Sea Turtles. Confidence Intervals for the Mean (Small Samples). Confidence Intervals for Population Proportions. Confidence Intervals for Variance and Standard Deviation.
7. Hypothesis Testing with One Sample continue 2e p-value approach.
Introduction to Hypothesis Testing. Hypothesis Testing for the Mean (Large Samples). Case Study: Human Body Temperature: What's Normal? Hypothesis Testing for the Mean (Small Samples). Hypothesis Testing for Proportions. Hypothesis Testing for the Variance and Standard Deviation.
8. Hypothesis Testing with Two Samples.
Testing the Difference Between Means (Large Independent Samples). Case Study: Oatbran and Cholesterol Level. Testing the Difference Between Means (Small Independent Samples). Testing the Difference Between Means (Dependent Samples). Testing the Difference Between Proportions.
9. Correlation and Regression.
Correlation. Linear Regression. Case Study: Correlation of Body Measurements. Measures of Regression and Prediction Intervals. Multiple Regression.
10. Chi-Square Tests and the F-Distribution.
Goodness of Fit. Independence. Case Study: Traffic Safety Facts. Comparing Two Variances. Analysis of Variance.
11. Nonparametric Tests.
The Sign Test. The Wilcoxon Tests. Case Study: Health and Nutrition. The Kruskal-Wallis Test. Rank Correlation. Runs Test.
Appendix A.
Alternative Presentation of the Standard Normal Distribution. Standard Normal Distribution Table (0-to-z). Alternate Presentation of the Standard Normal Distribution.
Appendix B.
Tables.