Illuminating statistical analysis using scenarios and simulations / Jeffrey E Kottemann, Ph.D.

Includes index.

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Illuminating Statistical Analysis Using Scenarios and Simulations; Contents; Preface; Acknowledgements; Part I: Sample Proportions and the Normal Distribution; 1: Evidence and Verdicts; 2: Judging Coins I; 3: Brief on Bell Shapes; 4: Judging Coins II; 5: Amount of Evidence I; 6: Variance of Evidence I; 7: Judging Opinion Splits I; 8: Amount of Evidence II; 9: Variance of Evidence II; 10: Judging Opinion Splits II; 11: It Has Been the Normal Distribution All Along; A Note on Stricter Thresholds for Type I Error; 12: Judging Opinion Split Differences; 13: Rescaling to Standard Errors

14: The Standardized Normal Distribution Histogram15: The z-Distribution; 16: Brief on Two-Tail Versus One-Tail; 17: Brief on Type I Versus Type II Errors; The Bigger Picture; Part II: Sample Means and the Normal Distribution; 18: Scaled Data and Sample Means; 19: Distribution of Random Sample Means; 20: Amount of Evidence; 21: Variance of Evidence; Variance and Standard Deviation; 22: Homing in on the Population Mean I; 23: Homing in on the Population Mean II; 24: Homing in on the Population Mean III; 25: Judging Mean Differences; 26: Sample Size, Variance, and Uncertainty

27: The t-DistributionPart III: Multiple Proportions and Means: The X- and F-Distributions; 28: Multiple Proportions and the X2-Distribution; 29: Facing Degrees of Freedom; 30: Multiple Proportions: Goodness of Fit; A Note on Using Chi-squared to Test the Distribution of a Scaled Variable; 31: Two-Way Proportions: Homogeneity; 32: Two-Way Proportions: Independence; 33: Variance Ratios and the F-Distribution; 34: Multiple Means and Variance Ratios: ANOVA; 35: Two-Way Means and Variance Ratios: ANOVA; Part IV: Linear Associations: Covariance, Correlation, and Regression; 36: Covariance

37: Correlation38: What Correlations Happen Just by Chance?; Special Considerations: Confidence Intervals for Sample Correlations; 39: Judging Correlation Differences; Special Considerations: Sample Correlation Differences; 40: Correlation with Mixed Data Types; 41: A Simple Regression Prediction Model; 42: Using Binomials Too; Getting More Sophisticated #1; Getting More Sophisticated #2; 43: A Multiple Regression Prediction Model; Getting More Sophisticated; 44: Loose End I (Collinearity); 45: Loose End II (Squaring R); 46: Loose End III (Adjusting R-Squared); 47: Reality Strikes

Part V: Dealing with Unruly Scaled Data48: Obstacles and Maneuvers; 49: Ordered Ranking Maneuver; 50: What Rank Sums Happen Just by Chance?; 51: Judging Rank Sum Differences; 52: Other Methods Using Ranks; 53: Transforming the Scale of Scaled Data; 54: Brief on Robust Regression; 55: Brief on Simulation and Resampling; Part VI: Review and Additional Concepts; 56: For Part I; 57: For Part II; 58: For Part III; 59: For Part IV; 60: For Part V; Appendices; A: Data Types and Some Basic Statistics; Some Basic Statistics (Primarily for Scaled and Binomial Variables)