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.
Assumptions:
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
