This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

Table of Contents:
Preface   1. Introduction to Statistics and Data Analysis 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 1.2 Sampling Procedures; Collection of Data 1.3 Measures of Location: The Sample Mean and Median             Exercises 1.4 Measures of Variability             Exercises 1.5 Discrete and Continuous Data 1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods 19 1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study             Exercises   2. Probability 2.1 Sample Space 2.2 Events             Exercises 2.3 Counting Sample Points             Exercises 2.4 Probability of an Event 2.5 Additive Rules             Exercises 2.6 Conditional Probability, Independence and Product Rules             Exercises 2.7 Bayes’ Rule             Exercises             Review Exercises 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   3. Random Variables and Probability Distributions 3.1 Concept of a Random Variable 3.2 Discrete Probability Distributions 3.3 Continuous Probability Distributions             Exercises 3.4 Joint Probability Distributions             Exercises             Review Exercises 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   4. Mathematical Expectation 4.1 Mean of a Random Variable             Exercises 4.2 Variance and Covariance of Random Variables             Exercises 4.3 Means and Variances of Linear Combinations of Random Variables 127 4.4 Chebyshev’s Theorem             Exercises             Review Exercises 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   5. Some Discrete Probability Distributions 5.1 Introduction and Motivation 5.2 Binomial and Multinomial Distributions             Exercises 5.3 Hypergeometric Distribution             Exercises 5.4 Negative Binomial and Geometric Distributions 5.5 Poisson Distribution and the Poisson Process             Exercises             Review Exercises 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   6. Some Continuous Probability Distributions 6.1 Continuous Uniform Distribution 6.2 Normal Distribution 6.3 Areas under the Normal Curve 6.4 Applications of the Normal Distribution             Exercises 6.5 Normal Approximation to the Binomial             Exercises 6.6 Gamma and Exponential Distributions 6.7 Chi-Squared Distribution 6.8 Beta Distribution 6.9 Lognormal Distribution (Optional) 6.10 Weibull Distribution (Optional)             Exercises             Review Exercises 6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   7. Functions of Random Variables (Optional) 7.1 Introduction 7.2 Transformations of Variables 7.3 Moments and Moment-Generating Functions             Exercises   8. Sampling Distributions and More Graphical Tools 8.1 Random Sampling and Sampling Distributions 8.2 Some Important Statistics             Exercises 8.3 Sampling Distributions 8.4 Sampling Distribution of Means and the Central Limit Theorem             Exercises 8.5 Sampling Distribution of S2 8.6 t-Distribution 8.7 F-Distribution 8.8 Quantile and Probability Plots             Exercises             Review Exercises 8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   9. One- and Two-Sample Estimation Problems 9.1 Introduction 9.2 Statistical Inference 9.3 Classical Methods of Estimation 9.4 Single Sample: Estimating the Mean 9.5 Standard Error of a Point Estimate 9.6 Prediction Intervals 9.7 Tolerance Limits             Exercises 9.8 Two Samples: Estimating the Difference Between Two Means 9.9 Paired Observations             Exercises 9.10 Single Sample: Estimating a Proportion 9.11 Two Samples: Estimating the Difference between Two Proportions             Exercises 9.12 Single Sample: Estimating the Variance 9.13 Two Samples: Estimating the Ratio of Two Variances             Exercises 9.14 Maximum Likelihood Estimation (Optional)             Exercises             Review Exercises 9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   10. One- and Two-Sample Tests of Hypotheses 10.1 Statistical Hypotheses: General Concepts 10.2 Testing a Statistical Hypothesis 10.3 The Use of P-Values for Decision Making in Testing Hypotheses             Exercises 10.4 Single Sample: Tests Concerning a Single Mean 10.5 Two Samples: Tests on Two Means 10.6 Choice of Sample Size for Testing Means 10.7 Graphical Methods for Comparing Means             Exercises 10.8 One Sample: Test on a Single Proportion 10.9 Two Samples: Tests on Two Proportions             Exercises 10.10 One- and Two-Sample Tests Concerning Variances             Exercises 10.11 Goodness-of-Fit Test 10.12 Test for Independence (Categorical Data) 10.13 Test for Homogeneity 10.14 Two-Sample Case Study             Exercises             Review Exercises 10.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   11. Simple Linear Regression and Correlation 11.1 Introduction to Linear Regression 11.2 The Simple Linear Regression Model 11.3 Least Squares and the Fitted Model             Exercises 11.4 Properties of the Least Squares Estimators 11.5 Inferences Concerning the Regression Coefficients 11.6 Prediction             Exercises 11.7 Choice of a Regression Model 11.8 Analysis-of-Variance Approach 11.9 Test for Linearity of Regression: Data with Repeated Observations 416             Exercises 11.10 Data Plots and Transformations 11.11 Simple Linear Regression Case Study 11.12 Correlation             Exercises             Review Exercises 11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   12. Multiple Linear Regression and Certain Nonlinear Regression Models 12.1 Introduction 12.2 Estimating the Coefficients 12.3 Linear Regression Model Using Matrices             Exercises 12.4 Properties of the Least Squares Estimators 12.5 Inferences in Multiple Linear Regression             Exercises 12.6 Choice of a Fitted Model through Hypothesis Testing 12.7 Special Case of Orthogonality (Optional)             Exercises 12.8 Categorical or Indicator Variables               Exercises 12.9 Sequential Methods for Model Selection 12.10 Study of Residuals and Violation of Assumptions 12.11 Cross Validation, Cp, and Other Criteria for Model Selection             Exercises 12.12 Special Nonlinear Models for Nonideal Conditions             Exercises             Review Exercises 12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   13. One-Factor Experiments: General 13.1 Analysis-of-Variance Technique 13.2 The Strategy of Experimental Design 13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA) 13.4 Tests for the Equality of Several Variances             Exercises 13.5 Multiple Comparisons             Exercises 13.6 Comparing a Set of Treatments in Blocks 13.7 Randomized Complete Block Designs 13.8 Graphical Methods and Model Checking 13.9 Data Transformations In Analysis of Variance)             Exercises 13.10 Random Effects Models 13.11 Case Study             Exercises             Review Exercises 13.12 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   14. Factorial Experiments (Two or More Factors) 14.1 Introduction 14.2 Interaction in the Two-Factor Experiment 14.3 Two-Factor Analysis of Variance             Exercises 14.4 Three-Factor Experiments             Exercises 14.5 Factorial Experiments for Random Effects and Mixed Models             Exercises             Review Exercises 14.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   15. 2k Factorial Experiments and Fractions 15.1 Introduction 15.2 The 2k Factorial: Calculation of Effects and Analysis of Variance 598 15.3 Nonreplicated 2k Factorial Experiment             Exercises 15.4 Factorial Experiments in a Regression Setting 15.5 The Orthogonal Design             Exercises 15.6 Fractional Factorial Experiments 15.7 Analysis of Fractional Factorial Experiments             Exercises 15.8 Higher Fractions and Screening Designs 15.9 Construction of Resolution III and IV Designs 15.10 Other Two-Level Resolution III Designs; The Plackett-Burman Designs 15.11 Introduction to Response Surface Methodology 15.12 Robust Parameter Design             Exercises             Review Exercises 15.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters   16. Nonparametric Statistics 16.1 Nonparametric Tests 16.2 Signed-Rank Test             Exercises 16.3 Wilcoxon Rank-Sum Test 16.4 Kruskal-Wallis Test             Exercises 16.5 Runs Test 16.6 Tolerance Limits 16.7 Rank Correlation Coefficient             Exercises             Review Exercises   17. Statistical Quality Control 17.1 Introduction 17.2 Nature of the Control Limits 17.3 Purposes of the Control Chart 17.4 Control Charts for Variables 17.5 Control Charts for Attributes 17.6 Cusum Control Charts             Review Exercises 18 Bayesian Statistics 18.1 Bayesian Concepts 18.2 Bayesian Inferences 18.3 Bayes Estimates Using Decision Theory Framework             Exercises   Bibliography A. Statistical Tables and Proofs B. Answers to Odd-Numbered Non-Review Exercises Index