Brian S. Everitt A Handbook of Statistical Analyses using SPSS

Display 9.1 Characteristics of 21 Titanic passengers.
In essence, this is the same question that is addressed by the multiple
regression model described in Chapter 4. Consequently, readers might
ask: What is different here? Why not simply apply multiple regression to
the Titanic data directly? There are two main reasons why this would not
be appropriate:
The response variable in this case is binary rather than continuous.
Assuming the usual multiple regression model for the probability
of surviving could lead to predicted values of the probability
outside the interval (0, 1).
The multiple regression model assumes that, given the values of
the explanatory variables, the response variable has a normal
distribution with constant variance. Clearly this assumption is not
acceptable for a binary response.
Consequently, an alternative approach is needed and is provided by
logistic regression, a brief account of the main points of which is given
in Box 9.1.
Box 9.1 Logistic Regression
In multiple linear regression (see Chapter 4), the expected
value of a response variable, y, is modeled as a linear function
of the explanatory variables:
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0 1 1 ...
E y x x
q q