The output may include the fitted logistic formula. At the bottom of Figure 18-4a, the formula

is shown as:

You can write out the formula manually by inserting the value of the regression coefficients from the

regression table into the logistic formula. The final model produced by the logistic regression program

from the data in Table 18-1 and the resulting logistic curve are shown in Figure 18-5.

Once you have the fitted logistic formula, you can predict the probability of having the outcome if you

know the value of the predictor variable. For example, if an individual is exposed to 500 REM of

radiation, the probability of the outcome is given by this formula: Probability of

, which equals 0.71. An individual exposed to 500 REM of

radiation has a predicted probability of 0.71 — or a 71 percent chance — of dying shortly thereafter.

The predicted probabilities for each individual are shown in the data listed in Figure 18-4b. You can

also calculate some points of special significance on a logistic curve, as you find out in the following

sections.

Be careful with your algebra when evaluating these formulas! The a coefficient in a logistic

regression is often a negative number, and subtracting a negative number is like adding its

absolute value.