Agresti/ Franklin: Statistics
TABLE OF CONTENTS
Note: each chapter concludes with Answers to Chapter Figure Questions; Chapter Summary; Summary of notation; Chapter Exercises.
1. Statistics: The Art and Science of Learning from Data
1.1 How Can You Investigate Using Data?
1.2 We Learn about Populations Using Samples
1.3 What Role do Computers Play in Statistics?
2. Exploring Data with Graphs and Numerical Summaries
2.1What Are the Types of Data?
2.2 How Can We Describe Data Using Graphical Summaries?
2.3 How Can We Describe the Center of Quantitative Data?
2.4 How Can We Describe the Spread of Quantitative Data?
2.5 How Can Measures of Position Describe Spread?
2.6 How Are Descriptive Summaries Misused?
3. Association: Contingency, Correlation, and Regression
3.1 How Can We Explore the Association between Two Categorical Variables?
3.2 How Can We Explore the Association between Two Quantitative Variables?
3.3 How Can We Predict the Outcome of a Variable
3.4 What are Some Cautions in Analyzing Associations?
4. Gathering Data
4.1 Should We Experiment or Should We Merely Observe?
4.2 What Are Good Ways and Poor Waysto Sample?
4.3 What Are Good Ways and Poor Waysto Experiment?
4.4 What Are Other Ways to Perform Experimental and Observational Studies?
5. Probability in our Daily Lives
5.1 How Can Probability Quantify Randomness?
5.2 How Can We Find Probabilities?
5.3 Conditional Probability: What's the Probability of A, Given B?
5.4 Applying the Probability Rules
6. Probability Distributions
6.1 How Can We Summarize Possible Outcomes and their Probabilities?
6.2 How Can We Find Probabilities for Bell-Shaped Distributions?
6.3 How Can We Find Probabilities when Each Observation Has Two Possible
6.4 How Likely Are the Possible Values of a Statistic?: The Sampling Distribution
6.5 How Close Are Sample Means to Population Means
6.6 How Can We Make Inferences about a Population?
7. Statistical Inference: Confidence Intervals
7.1 What Are Point and Interval Estimates of Population Parameters?
7.2 How Can We Construct a Confidence Interval to Estimate a Population Proportion?
7.3 How Can We Construct a Confidence Interval to Estimate a Population Mean?
7.4 How Do We Choose the Sample Size for a Study
7.5 How Do Computers Make New Estimation Methods Possible?
8. Statistical Inference: Significance Tests about Hypotheses
8.1 What Are the Steps for Performing a Significance Test?
8.2 Significance Tests about Proportions
8.3 Significance Tests about Means
8.4 Decisions and Types of Errors in Significance Tests
8.5 Limitations of Significance Tests
8.6 How Likely Is Type II Error (Not Rejecting H0, Even though it's False)?
9. Comparing Two Groups
9.1 Categorical Response: How Can We Compare Two Proportions?
9.2 Quantitative Response: How Can We Compare Two Means?
9.3 Other Ways of Comparing Means and Comparing Proportions
9.4 How Can We Analyze Dependent Samples?
9.5 How Can We Adjust for Effects of Other Variables?
10. Analyzing the Association Between Categorical Variables
10.1 What Is Independenceand What Is Association?
10.2 How Can We Test whether Categorical Variables are Independent?
10.3 How Strong is the Association?
10.4 How Can Residuals Reveal the Pattern of Association?
10.5 What if the Sample Size is Small? Fisher's Exact Test
11. Analyzing Association between Quantitative Variables: Regression Analysis
11.1 How Can We Model How Two Variables Are Related?
11.2 How Can We Describe Strength of Association?
11.3 How Can We Make Inferences about the Association?
11.4 What Do We Learn from How the Data Vary around the Regression Line?
11.5 Exponential Regression: A Model for Nonlinearity
12. Multiple Regression
12.1 How Can We Use Several Variables to Predict a Response?
12.2 Extending the Correlation and R-squared for Multiple Regression
12.3 How Can We Use Multiple Regression to Make Inferences?
12.4 Checking a Regression Model Using Residual Plots
12.5 How Can Regression Include Categorical Predictors?
12.6 How Can We Model a Categorical Response?
13. Comparing Groups: Analysis of Variance Methods
13.1 How Can We Compare Several Means?: One-Way ANOVA
13.2 How Should We Follow Up an ANOVA F test
13.3 What if there Are Two Factors?: Two-way ANOVA
14. Nonparametric Statistics
14.1 How Can We Compare Two Groups by Ranking?
14.2 Nonparametric Methods for Several Groups and for Matched Pairs
Appendices
Appendix A: Tables
Appendix B: Selected Answers
Index
Photo Credits
