Research Catalog

Title

Common errors in statistics (and how to avoid them) / Phillip I. Good, James W. Hardin.

Author

Good, Phillip I.

Publication

Hoboken, N.J. : Wiley, ©2006.

Items in the Library & Off-site

Details

Additional Authors

Hardin, James W. (James William)

Description

xi, 254 pages : illustrations; 23 cm

Summary

Common Errors is consistently coherent and provides a consistent level throughout. The Second Edition elaborates on a few key topics such as testing hypotheses (two-way designs) and multivariate regression (nonuniqueness and indicator variables). --Publisher.

Subject

• Statistics
• statistics
• Statistics
• Statistik
• Fehlerverhütung
• Statistiek
• Fouten

Genre/Form

• Statistics
• Statistics.
• Statistiques.

Bibliography (note)

• Includes bibliographical references (p. 223-242) and indexes.

Contents

• Preface -- pt. 1. Foundations -- 1. Sources of error -- Prescription -- Fundamental concepts -- Ad hoc, post hoc hypotheses -- 2. Hypotheses : the why of your research -- Prescription -- What is a hypothesis? -- How precise must a hypothesis be? -- Found data -- Null hypothesis -- Neyman-Pearson theory -- Deduction and induction -- Losses -- Decisions -- To learn more -- 3. Collecting data -- Preparation -- Measuring devices -- Determining sample size -- Fundamental assumptions -- Experimental design -- Four guidelines -- Are experiments really necessary? -- To learn more.
• pt. 2. Hypothesis testing and estimation -- 4. Estimation -- Prevention -- Desirable and not-so-desirable estimators -- Interval estimates -- Improved results -- Summary -- To learn more -- 5. Testing hypotheses : choosing a test statistic -- Comparing means of two populations -- Comparing variances -- Comparing the means of K samples -- Higher-order experimental designs -- Contingency tables -- Inferior tests -- Multiple tests -- Before you draw conclusions -- Summary -- To learn more -- 6. Strengths and limitations of some miscellaneous statistical procedures -- Bootstrap -- Bayesian methodology -- Meta-analysis -- Permutation tests -- To learn more -- 7. Reporting your results -- Fundamentals -- Tables -- Standard error -- p-values -- Confidence intervals -- Recognizing and reporting biases -- Reporting power -- Drawing conclusions -- Summary -- To learn more -- 8. Interpreting reports -- With a grain of salt -- Rates and percentages -- Interpreting computer printouts -- 9. Graphics -- The soccer data -- Five rules for avoiding bad graphics -- One rule for correct usage of three-dimensional graphics -- One rule for the misunderstood pie chart -- Two rules for effective display of subgroup information -- Two rules for text elements in graphics -- Multidimensional displays -- Choosing effective display elements -- Choosing graphical displays -- Summary -- To learn more.
• pt. 3. Building a model -- 10. Univariate regression -- Model selection -- Estimating coefficients -- Further considerations -- Summary -- To learn more -- 11. Alternate methods of regression -- Linear vs. Nonlinear regression -- Least absolute deviation regression -- Errors-in-variables regression -- Quantile regression -- The ecological fallacy -- Nonsense regression -- Summary -- To learn more -- 12. Multivariable regression -- Caveats -- Factor analysis -- Generalized linear models -- Reporting your results -- A conjecture -- Building a successful model -- To learn more -- 13. Validation -- Methods of validation -- Measures of predictive success -- Long-term stability -- To learn more -- appendix A.A note on screening regression equations -- appendix B. Cross-validation, the jackknife, and the bootstrap : excess error estimation in forward logistic regression -- Glossary, grouped by related but distinct terms -- Bibliography -- Author index-- Subject index.

ISBN

• 0471794317
• 9780471794318

LCCN

• 2005057519
• 9780471794318

OCLC

• ocm62408850
• 62408850
• SCSB-1412606

Owning Institutions

Princeton University Library