Logistic regression is the workhorse of binary outcome research across health sciences, social sciences, and management. When your dependent variable is categorical — did the patient recover or not, did the customer churn or not, did the student pass or fail — logistic regression estimates the probability of the outcome as a function of one or more predictor variables.
The output of logistic regression is expressed as odds ratios (ORs). An OR greater than 1 means the predictor is associated with higher odds of the outcome; an OR less than 1 means lower odds. Unlike relative risks, odds ratios do not directly tell you the probability difference — they require conversion when the outcome is common.
Model assumptions for logistic regression include: the outcome is binary, the observations are independent, there is little multicollinearity among predictors, and there are no extreme outliers. Unlike linear regression, logistic regression does not assume normality of residuals or homoscedasticity. Check for multicollinearity using variance inflation factors (VIF < 5 is acceptable).
Model fit for logistic regression is assessed using the Hosmer-Lemeshow test (good fit = non-significant), the Nagelkerke R² (pseudo R²), and classification accuracy (% correctly predicted). Report the -2 log likelihood and compare nested models using the likelihood ratio chi-square test.
We provide logistic regression consulting from data preparation through to full results write-up. Our consultants handle variable selection, assumption checking, model building, and interpretation of odds ratios with 95% confidence intervals. Output is formatted to APA style or your target journal’s requirements.
