![]() ![]() 6 Practical Regression Topics 1: Multi-level factors, contrast coding, interactions.5.7 Appendix: Other Generalized Linear Models.5.4 Model criticism for logistic regression.5.3.1 Likelihood ratio test: General case. ![]() 5.2 Evaluating logistic regression models.5.1.5 Fitting a logistic regression model.5.1.4 Differences from linear regression: Fitting and interpretation.5.1.2 Interpreting the coefficients: Logit, odds, and probability.4 Categorical data analysis: Preliminaries.3.6.2 Linear regression assumptions: Solutions.3.6.1 Multiple linear regression: Solutions.3.5.5 Interim recipe: Building a multiple linear regression model.3.4.13 Regression assumptions: Reassurance.3.4.8 Assumption 5: Linear independence of predictors.3.4.5 Assumtion 4: Constancy of variance.3.4.4 Assumption 3: Normality of errors.3.4.3 Assumption 2: Independence of errors.3.3.4 Categorical factors with more than two levels.3.2.6 SLR with a binary categorical predictor vs. two-sample \(t\)-test.3.1.3 Steps and assumptions of regression analysis.2.5.3 Parametric versus non-parametric tests.2.3.4 Unequal variances: Welch \(t\)-test.1.1.3 Sampling from a non-normal distribution.1.1.2 Sampling distribution of the sample mean.1.1.1 Sample \(\to\) population: High level.Quantitative Methods for Linguistic Data. ![]()
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