Statistics Examples

Example 7.7: Tabulate binomial probabilities
Example 7.8: Plot two empirical cumulative density functions using available tools
Example 7.11: Plot an empirical cumulative distribution function from scratch
Example 7.12: Calculate and plot a running average
Example 7.16: Assess robustness of permutation test to violations of exchangeability assumption
Example 7.17: The Smith College diploma problem
Example 7.18: Displaying missing value categories in a table
Example 7.34: Propensity scores and causal inference from observational studies
Example 7.35: Propensity score matching
Example 7.36: Prospensity score stratification
Example 7.37: Calculation of Hotelling's T^2
Example 7.38: Kaplan-Meier survival estimates
Example 7.39: Nelson-Aalen estimate of cumulative hazard
Example 7.42: Testing Cox model proportionality assumption

Example 8.6: Changing the reference category for categorical variables
Example 8.7: Hosmer and Lemeshow goodness-of-fit
Example 8.8: more Hosmer and Lemeshow
Example 8.9: Contrasts
Example 8.14: generating standardized regression coefficients
Example 8.15: Firth logistic regression
Example 8.16: Exact logistic regression
Example 8.17: Logistic regression via MCMC
Example 8.18: A Monte Carlo experiment
Example 8.21: latent class analysis
Example 8.22: latent class modeling using randomLCA
Example 8.23: expanding latent class model results
Example 8.25: more latent class models (plus a graphical display)
Example 8.29: risk ratios and odds ratios
Example 8.30: Compare Poisson and negative binomial count models
Example 8.32: The HistData package, sunflower plots, and getting data from R into SAS
Example 8.34: Robustness of the t test with small n
Example 8.42: Skewness and kurtosis and more moments

Example 9.2: Transparent overplotting and bivariate KDE
Example 9.4: Proc MI and fully conditional specification
Example 9.5: Finite mixture models with concomitant variables
Example 9.7: New stuff in SAS 9.3-- Frailty models
Example 9.8: New stuff in SAS 9.3-- Bayesian random effects models in Proc MCMC
Example 9.10: more regression trees and recursive partitioning with "partykit"
Example 9.12: simpler ways to carry out permutation tests
Example 9.13: Negative binomial regression with proc mcmc
Example 9.14: confidence intervals for logistic regression models
Example 9.24: Changing the parameterization for categorical predictors
Example 9.30: addressing multiple comparisons
Example 9.31: Exploring multiple testing procedures
Example 9.32: Multiple testing simulation
Example 9.33: Multiple imputation, rounding, and bias
Example 9.35: Discrete randomization and formatted output
Example 9.36: Levene's test for equal variances
Example 9.37: (Mis)behavior of binomial confidence intervals