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Hypothesis testing

• Wald test for linear constraints
• Wald test (delta method) for nonlinear constraints
• Likelihood-ratio test after any ML estimation
• Bonferroni, Holm, and Šidák adjustments for multiple comparisons

Generalized testing

• Ability to combine separate estimates into one combined estimate
• Robust covariance matrix of combined estimates
• Tests of linear and nonlinear combinations of estimates across fitted models
• Point estimates and confidence intervals of linear and nonlinear combinations of estimates across fitted models

Predictions

• Ability to obtain predicted values after all estimation commands
• Predictor types that are tightly coupled to the estimation command
• Default predicted value that is most relevant to the fitted model

Generalized predictions

• Linear and nonlinear combinations of
  • Standard predictions
  • Equation index values
  • Estimated coefficients
  • Data
• Inferential statistics for generalized predictions:
  • Point estimates
  • Standard errors
  • Variances
  • Wald test statistics
  • Significance levels
  • Pointwise confidence intervals

Postestimation statistics

• Estimation sample summary statistics
• Akaike (AIC, CAIC, AICc) and Bayesian information criteria Updated
• Covariance matrix analysis

Linear and nonlinear combinations of coefficients

• Point estimates
• Standard errors
• Confidence intervals
• Tests of significance
• Covariances of transformations
• Support for survey and clustered data

Effect sizes

• Comparison of means
  • Cohen's d
  • Hedge's g
  • Glass's Δ
  • Point/biserial correlation
  • Confidence intervals
• Variance explained by regression and ANOVA
  • Eta-squared—η²
  • Omega-squared—ω²
  • Confidence intervals
  Watch A tour of effect sizes.