21. When nominal variables are to be explained, logistic regression or probit regression is commonly used. 22. Compute p ( success ) for each subject using the coefficients from the logistic regression . 23. It is similar to linear regression and logistic regression . 24. Rosner ( 1992 ) shows that the ratio methods apply approximately to logistic regression models. 25. However, logistic regression cannot be handled this way. 26. Discriminative : Logistic regression , Support Vector Machines, Maximum Entropy Markov Model, Conditional Random Fields, Neural Networks 27. Two measures of deviance are particularly important in logistic regression : null deviance and model deviance. 28. Age remains significant in multiple logistic regression analyses. 29. Logistic regression can take into account stratification by having a different constant term for each strata.30. If the dependent variable is a dummy variable, then logistic regression or probit regression is commonly employed.
