'This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings
' Introduction to the bootstrap method as a simple yet powerful tool that is integrated with general inferential procedures (Monte Carlo methods are also introduced.) Many exercises that enrich the book. (Some are relatively simple and reinforce calculations. Others concern bootstrap and Monte Carlo methods and theoretical material on survey sampling. Many incorporate use of the computer.) Nearly 100 new problems, including many that are substantial enough to form the basis for computer labs. Examples from contemporary, high interest areas such as genomics and financial statistics complement already interesting existing applications (e.g., probability of AIDS infection, state lotteries, polygraph testing) and graphical displays. There is new treatment of the topic of loglinear smoothing. Treatment of Bayesian inference is now presented in parallel with frequentist methods.
'1. PROBABILITY. Introduction. Sample Spaces. Probability Measures. Computing Probabilities: Counting Methods. Conditional Probability. Independence. Concluding Remarks. Problems. 2. RANDOM VARIABLES. Discrete Random Variables. Continuous Random Variables. Functions of a Random Variable. Concluding Remarks. Problems. 3. JOINT DISTRIBUTIONS. Introduction. Discrete Random Variables. Continuous Random Variables. Independent Random Variables. Conditional Distributions. Functions of Jointly Distributed Random Variables. Extrema and Order Statistics. Problems. 4. EXPECTED VALUES. The Expected Value of a Random Variable. Variance and Standard Deviation. Covariance and Correlation. Conditional Expectation and Prediction. The Moment-Generating Function. Approximate Methods. Problems. 5. LIMIT THEOREMS. Introduction. The Law of Large Numbers. Convergence in Distribution and the Central Limit Theorem. Problems . 6. DISTRIBUTIONS DERIVED FROM THE NORMAL DISTRIBUTION. Introduction. Chi-Squared, t, and F Distributions. The Sample Mean and Sample Variance. Problems. 7. SURVEY SAMPLING. Introduction. Population Parameters. Simple Random Sampling. Estimation of a Ratio. Stratified Random Sampling. Concluding Remarks. Problems. 8. ESTIMATION OF PARAMETERS AND FITTING OF PROBABILITY DISTRIBUTIONS. Introduction. Fitting the Poisson Distribution to the Emissions of Alpha Particles. Parameter Estimation. The Method of Moments. The Method of Maximum Likelihood. The Bayesian Approach to Parameter Estimation. Efficiency and the Cramer-Rao Lower Bound. Sufficiency. Concluding Remarks. Problems. 9. TESTING HYPOTHESES AND ASSESSING GOODNESS OF FIT. Introduction. The Neyman-Pearson Paradigm. The Duality of Confidence Intervals and Hypothesis Tests. Generalized Likelihood Ratio Tests. Likelihood Ratio Tests for the Multinomial Distribution. The Poisson Dispersion Test. Hanging Rootograms. Probability Plots. Tests for Normality. Concluding Remarks. Problems. 10. SUMMARIZING DATA. Introduction. Methods Based on the Cumulative Distribution Function. Histograms, Density Curves, and Stem-and-Leaf Plots. Measures of Location. Measures of Dispersion. Boxplots. Exploring Relationships with Scatterplots. Concluding Remarks. Problems. 11. COMPARING TWO SAMPLES. Introduction. Comparing Two Independent Samples. Comparing Paired Samples. Experimental Design. Concluding Remarks. Problems. 12. THE ANALYSIS OF VARIANCE. Introduction. The One-Way Layout. The Two-Way Layout. Concluding Remarks. Problems. 13. THE ANALYSIS OF CATEGORICAL DATA. Introduction. Fisher''s Exact Test. The Chi-Square Test of Homogeneity. The Chi-Square Test of Independence. Matched-Pairs Designs. Odds Ratios. Concluding Remarks. Problems. 14. LINEAR LEAST SQUARES. Introduction. Simple Linear Regression. The Matrix Approach to Linear Least Squares. Statistical Properties of Least Squares Estimates. Multiple Linear RegressionAn Example. Conditional Inference, Unconditional Inference, and the Bootstrap. Concluding Remarks. Problems. 15. DECISION THEORY AND BAYESIAN INFERENCE. Introduction. Decision Theory. The Subjectivist Point of View. Concluding Remarks. Problems. Appendix A. Common Distributions. Appendix B. Tables. Bibliography. Answers to Selected Problems. Author Index. Index to Data Sets. Subject Index.
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