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Logistic Multiple Regression


Used for multiple regression models where the dependent variable is dichotomous (0 or 1 values) or ordinal if conducting multinomial logistic regression. By convention, the dependent variable should be coded 0 and 1 where 1 represents the value of greatest interest.




See multiple regression for a more complete list of ordinary least squares (OLS) regression assumptions.  In addition to the key characteristic that the dependent variable is discrete rather than continuous, the following assumptions for logistic regression differ from those for OLS regression:


Specification error - Linearity


Linearity is not required in logistic regression between the independent and dependent variables.  It does require that the logits of the independent and dependent variables are linear. If not linear, the model may not find statistical significance when it actually exists (Type II error).


Large Sample Size


Unlike OLS regression, large sample size is needed for logistic regression.  If there is difficulty in converging on a solution, a large number of iterations, or very large regression coefficients, there may be insufficient sample size.


Software Output Example



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