2. Hypotheses : the why of your research
Pt. II. Statistical analysis
4. Data quality assessment
6. Testing hypotheses : choosing a test statistic
7. Miscellaneous statistical practices
8. Reporting your results
11. Univariate regression
12. Alternate methods of regression
13. Multivariable regression
14. Modeling correlated data
Ad hoc, post hoc hypotheses
2. Hypotheses : the why of your research
How precise must a hypothesis be?
Are experiments really necessary?
pt. 2. Hypothesis testing and estimation
Desirable and not-so-desirable estimators
5. Testing hypotheses : choosing a test statistic
Comparing means of two populations
Comparing the means of K samples
Higher-order experimental designs
Before you draw conclusions
6. Strengths and limitations of some miscellaneous statistical procedures
7. Reporting your results
Recognizing and reporting biases
Interpreting computer printouts
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
10. Univariate regression
11. Alternate methods of regression
Linear vs. Nonlinear regression
Least absolute deviation regression
Errors-in-variables regression
12. Multivariable regression
Generalized linear models
Building a successful model
Measures of predictive success
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
2. Hypotheses: the why of your research
II: Hypothesis testing and estimation
5. Testing hypotheses: choosing a test statistic
6. Strengths and limitations of some miscellaneous statistical procedures
7. Reporting your results
10. Multivariable regression
