Transforming variables to meet an assumption

Transforming variables can be done to correct for outliers and assumption failures (normality, linearity, and homoscedasticity/homogeneity); however, interpretation is then limited to the transformed scores. Normality assumes that the dependent variables are normally distributed (symmetrical bell shaped) for each group Homogeneity of variance assumes that groups have equal error variances Linearity assumes a straight line

Table and Symbols in a Logistic Regression

Source B SE B Wald χ2 p OR 95% CI OR Variable 1 1.46 0.12 7.55 .006 4.31 [3.26, 5.35] Variable 2 -0.43 0.15 6.31 .012 0.65 [0.18, 0.83] Note. OR = odds ratio. CI = confidence interval The table for a typical logistic regression is shown above.  There are six sets of symbols used