Illuminating statistical analysis using scenarios and simulations /
"Features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference. Illuminating Statistical Analysis Using Scenarios and Simulations presents the basic conc...
Saved in:
| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2017]
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| Series: | Online access with DDA: Askews (Maths)
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- 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).
