Sample size and cases with negative and positive outcome 

First the program gives sample size and the number and proportion of cases with a negative (Y=0) and 

positive (Y=1) outcome.   

Overall model fit 

The  null model  2 Log Likelihood is given by  2 * ln(L

0

) where L

0

 is the likelihood of obtaining the 

observations if the independent variables had no effect on the outcome. 

The full model  2 Log Likelihood is given by  2 * ln(L) where L is the likelihood of obtaining the observations 

with all independent variables incorporated in the model. 

The difference of these two yields a Chi Square statistic, which is a measure of how well the independent 

variables affect the outcome or dependent variable. 

If the P value for the overall model fit statistic is less than the conventional 0.05 then there is evidence that 

at least one of the independent variables contributes to the prediction of the outcome.  

Regression coefficients 

The regression coefficients are the coefficients b

0

, b

1

, b

2

, ... b

k

 of the regression equation: 

b

=

Y  

b

+

 X

0

1

1

b

+

2

 X

2

b

+

3

X

3

b

+

. 

. 

.

+

k

X

k

An independent variable with a regression coefficient not significantly different from 0 (P>0.05) can be 

removed from the regression model (press function key F7 to repeat the logistic regression procedure).  If 

P<0.05 then the variable contributes significantly to the prediction of the outcome variable. 

The logistic regression coefficients show the change (increase when b

i

>0, decrease when b

i

<0) in the 

predicted logged odds of having the characteristic of interest for a one unit change in the independent 

variables. 

When the independent variables X

a

 and X

b

 are dichotomous variables (e.g. Smoking, Sex) then the 

influence of these variables on the dependent variable can simply be compared by comparing their 

regression coefficients b

a

 and b

b

. 

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