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| Introduction | |
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| A Brief History | |
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| Some Examples | |
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| A Chapter Summary | |
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| Probability | |
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| Sample Spaces and the Algebra of Sets | |
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| The Probability Function | |
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| Discrete Probability Functions | |
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| Continuous Probability Functions | |
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| Conditional Probability | |
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| Independence | |
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| Repeated Independent Trials | |
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| Combinatorics | |
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| Combinatorial Probability | |
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| Random Variables | |
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| The Probability Density Function | |
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| The Hypergeometric and Binomial Distributions | |
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| The Cumulative Distribution Function | |
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| Joint Densities | |
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| Independent Random Variables | |
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| Combining and Transforming Random Variables | |
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| Order Statistics | |
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| Conditional Densities | |
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| Expected Values | |
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| Properties of Expected Values | |
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| The Variance | |
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| Properties of Variances | |
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| Chebyshev's Inequality | |
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| Higher Moments | |
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| Moment-Generating Functions | |
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| Minitab Applications | |
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| Special Distributions | |
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| The Poisson Distribution | |
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| The Normal Distribution | |
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| The Geometric Distribution | |
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| The Negative Binomial Distribution | |
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| The Gamma Distribution | |
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| Minitab Applications | |
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| A Proof of the Central Limit Theorem | |
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| Estimation | |
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| Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments | |
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| Interval Estimation | |
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| Properties of Estimators | |
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| Minimum-Variance Estimators: The Cramer-Rao Lower Bound | |
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| Sufficiency | |
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| Consistency | |
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| Minitab Applications | |
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| Hypothesis Testing | |
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| The Decision Rule | |
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| Testing Binomial Data-H0: p = p | |
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| Type I and Type II Errors | |
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| A Notion of Optimality: The Generalized Likelihood Ratio | |
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| The Normal Distribution | |
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| Point Estimates for ï¿½Ç m and ï¿½Ç s2 | |
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| The ï¿½Ç c2 Distribution | |
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| Inferences about ï¿½Ç s2 | |
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| The F and t Distributions | |
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| Drawing Inferences about ï¿½Ç m | |
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| Minitab Applications | |
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| Some Distribution Results for Y and S | |
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| Appendix 7.A.3: A Proof of Theorem 7.3.5 | |
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| A Proof That the One-Sample t | |
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| Test Is a GLRT | |
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| Types of Data: A Brief Overview | |
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| Classifying Data | |
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| Two-Sample Problems | |
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| Testing H 0: = The Two-Sample t Test | |
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| Testing H 0: = The F Test | |
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| Binomial Data: Testing H 0 px = py | |
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| Confidence Intervals for the Two-Sample Problem | |
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| A Derivation of the Two-Sample t | |
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| Test (A Proof of Theorem 9.2.2.) | |
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| Power Calculations for a Two-Sample t Test | |
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| Minitab Applications | |
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| Goodness-of-Fit Tests | |
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| The Multinomial Distribution | |
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| Goodness-of-Fit Tests: All Parameters Known | |
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| Goodness-of-Fit Tests: Parameters Unknown | |
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| Contingency Tables | |
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| Minitab Applications | |
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| Regression | |
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| The Method of Least Squares | |
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| The Linear Model | |
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| Covariance and Correlation | |
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| The Bivariate Normal Distribution | |
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| Minitab Applications | |
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| A Proof of Theorem 11.3.3 | |
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| The Analysis of Variance | |
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| The F Test | |
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| Multiple Comparisons: Tukey's Method | |
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| Testing Subhypotheses with Orthogonal Contrasts | |
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| Data Transformations | |
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| Minitab Applications | |
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| A Proof of Theorem 12.2.2 | |
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| The Distribution of $E{ down 12 SSTR/ up 12 (k-1)} over { down 12 SSE/ up 12 (n-k)} | |
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| When H1 Is True | |
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| Randomized Block Designs | |
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| The F Test for a Randomized Block Design | |
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| The Paired t Test | |
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| Minitab Applications | |
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| Nonparametric Statistics | |
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| The Sign Test | |
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| The Wilcoxon Signed Rank Test | |
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| The Kruskal-Wallis Test | |
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| The Friedman Test | |
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| Minitab Applications | |
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| Appendix: Statistical Tables | |
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| Answers to Selected Odd-Numbered Questions | |
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| Bibliography | |
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| Index | |