Nathan Tintle attained his Ph.D. in Statistics in 2005 from Stony Brook University and has been the primary mentor for over 95 undergraduate students in extended summer and/or academic year research projects in statistics, with the bulk of these students conducting research in statistical genetics and biostatistics.
Preliminaries Introduction to Statistical Investigations 1
Section P.1 Introduction to the Six-Step Method 2
Section P.2 Exploring Data 6
Section P.3 Exploring Random Processes 10
Unit 1 Four Pillars of Inference: Strength, Size, Breadth, and Cause 21
Chapter 1 Significance: How Strong is the Evidence? 22
Section 1.1 Introduction to Chance Models 23
Section 1.2 Measuring the Strength of Evidence 35
Section 1.3 Alternative Measure of Strength of Evidence 46
Section 1.4 What Impacts Strength of Evidence? 53
Section 1.5 Inference for a Single Proportion: Theory-Based Approach 63
Chapter 2 Generalization: How Broadly Do the Results Apply? 102
Section 2.1 Sampling from a Finite Population 103
Section 2.2 Inference for a Single Quantitative Variable 120
Section 2.3 Errors and Significance 138
Chapter 3 Estimation: How Large is the Effect? 163
Section 10.1 Two Quantitative Variables: Scatterplots and Correlation 521
Section 10.2 Inference for the Correlation Coefficient: Simulation-Based Approach 529
Section 10.3 Least Squares Regression 538
Section 10.4 Inference for the Regression Slope: Simulation-Based Approach 547
Section 10.5 Inference for the Regression Slope: Theory-Based Approach 552
Appendix A Calculation Details 592
Appendix B Stratified and Cluster Samples 610
Solutions to Selected Exercises 615
Index 668
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