Common Errors in Statistics (and How to Avoid Them)

17.07.2009

Englisch

9780470457986

John Wiley & Sons

Nein

Phillip I. Good, James W. Hardin

288

3

Broschiert

PHILLIP I. GOOD, PhD, is Operations Manager of Statcourse.com, a consulting firm specializing in statistical solutions for industry. He has published more than thirty scholarly works, more than six hundred popular articles, and twenty-one books, including Introduction to Statistics Through Resampling Methods and R/S-PLUS(r) and Introduction to Statistics Through Resampling Methods and Microsoft Office Excel(r), both published by Wiley. JAMES W. HARDIN, PhD, is Research Associate Professor and Director of the Biostatistics Collaborative Unit at the University of South Carolina.

Statistik

Preface. PART I: FOUNDATIONS. 1. Sources of Error. 1. Prescription. 2. Fundamental Concepts. 3. Ad-hoc, post-hoc hypotheses. 2. Hypotheses: The Why of Your Research. 1. Prescription. 2. What is a hypothesis? 3. How precise must a hypothesis be? 4. Found data. 5. Null hypothesis. 6. Neyman-Pearson theory. 7. Deduction and Induction. 8. Losses. 9. Decisions. 10. To Learn More. 3. Collecting Data. 1. Preparation. 2. Response Variables. 3. Determining Sample Size. 4. Fundamental Assumptions. 5. Experimental Design. 6. Four Guidelines. 7. Are Experiments Really Necessary? 8. To Learn More. PART II: STATISTICAL ANALYSIS. 4. Data Quality Assessment. 1. GIGO. 2. Objectives. 3. Design Review. 4. Data Review. 5. Estimation. 1. Prevention. 2. Desirable and Not-so-desirable estimators. 3. Interval Estimates. 4. Improved Results. 5. Summary. 6. To Learn More. 6. Testing Hypotheses: Choosing a Test Statistic. 1. First Steps. 2. Test Assumptions. 3. Binomial Trials. 4. Categorical Data. 5. Time to Event Data (survival analysis). 6. Comparing Means of Two Sets of Measurements. a. Multivariate comparisons. b. Options. c. Testing equivalence. d. Unequal variances. e. Dependent observations. 7. Comparing Variances. 8. Comparing the Means of K Samples. 9. Subjective Data. 10. Independence vs. Correlation. 11. Higher Order Experimental Designs. f. Errors in interpretation. g. Multi-factor designs. h. Cross-over designs. i. Factorial designs. j. Unbalanced designs. 12. Inferior Tests. 13. Multiple Tests. 14. Summary. 15. To Learn More. 7. Miscellaneous Statistical Procedures. 1. Bootstrap. 2. Bayesian Methodology. 3. Meta-Analysis. 4. Permutation Tests. 5. To Learn More. PART III: REPORTS. 8. Reporting Results. 1. Fundamentals. a. Treatment Allocation. b. Adequacy of Blinding. c. Missing Data. 2. Descriptive Statistics. d. Binomial trials. e. Categorical data. f. Rare events. g. Measurements. h. Which mean? i. Ordinal data. j. Tables. k. Dispersion, precision, and accuracy. 3. Standard Error. 4. p-values. 5. Confidence Intervals. 6. Recognizing and Reporting Biases. 7. Reporting Power. 8. Drawing Conclusions. 9. Summary. 10. To Learn More. 9. Interpreting Reports. 1. With A Grain of Salt. 2. Rates and Percentages. 3. Interpreting Computer Printouts. 10. Graphics. 1. Five Rules for Avoiding Bad Graphics. 2. Displaying the 3rd Dimension. 3. The Misunderstood Pie Chart. 4. Effective Display of Subgroup Information. 5. Two Rules for Text Elements. 6. Choosing The Right Display. 7. To Learn More. PART IV: BUILDING A MODEL. 11. Univariate Regression. 1. Model Selection. a. Scope. b. Ambiguous relationships. c. Confounding variables. 2. Estimating Coefficients. 3. Further Considerations. a. Bad data. b. Practical v. statistical significance. c. Goodness-of-fit v. prediction. d. Indicator variables. e. Transformations. f. When a straight line won't do. g. Curve fitting and Magic Beans. 4. Summary. 5. Checklist. 6. To Learn More. 12. Alternate Modeling Methods. 1. LAD Regression. 2. Demming or EIV Regression. 3. Quantile Regression. 4. The Ecological Fallacy. 5. Poisson and Negative Binomial Regression. 6. Nonsense Regression. 7. Summary. 8. To Learn More. 13. Multivariate Regression. 1. Caveats. 2. Correcting for Confounding Variables. 3. Keep it Simple. 4. Dynamic Models. 5. Factor Analysis. 6. Reporting Your Results. 7. A Conjecture. 8. Decision Trees. 9. Building a Successful Model. 10. To Learn More. 14. Modeling Correlated Data. 2. Common Sources of Error. 3. Panel Data. 4. Fixed and Random Effects Models. 5. GEE's. a. Subject-specific or population averaged? b. Variance estimation. 6. Quick Reference for Popular Panel Estimators. 7. To Learn More. 15. Validation. 1. Objectives. 2. Methods of Validation. a. Independent verification. b. Split sample. c. Resampling. 3. Measures of Predictive Success. 4. Long Term Stability. 5. To Learn More. Appendix A. Appendix B. Appendix C. Glossary. Bibliography. Author Index. Subject Index.

238 mm

16.2 cm