ещё о логистической

Logistic regression can be used to predict a dependent variable on the basis of continuous and/or categorical independents and to determine the effect size of the independent variables on the dependent; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables. The impact of predictor variables is usually explained in terms of odds ratios.

...  logistic regression does not assume linearity of relationship between the independent variables and the dependent, does not require normally distributed variables, does not assume homoscedasticity, and in general has less stringent requirements.
from menu:

Analyze - Regression - Binary Logistic

Analyze - Regression - Multinomial Logistic

Logit regression, discussed separately, is another related option in SPSS for using loglinear methods to analyze one or more dependents.

BINARY LOGISTIC REGRESSION: DEPENDENTS	OUTCOME:
Binary variable is entered as a dependent	Highest is predicted, lowest is reference
	The reference level cannot be changed.
MULTINOMIAL LOGISTIC REGRESSION: DEPENDENTS
Binary or multinomial variable entered as dependent	Highest is reference, all others compared to it by default.
	Click "Reference Category" button to override the default.

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