**Multi-way Regression with
Prevalence as the Outcome**

This exercise repeats the previous exercise for running model 7, but instead of using
the outcome variable **waz**, the outcome variable is switched to a dummy for
underweight (<-2 SD weight /age) yes/no called **waprev**, which yields a
prevalence outcome. Follow these steps to run the regression model:

1. Open

keast4j.sav2. Click on

Statistics, Regression, Linear...3. Enter the variable

waprevfrom the variable list into the box labeledDependentusing the arrow key.4. Enter these variables, one-by-one into the

Independent(s)box using the arrow key:dlowedn, dbadro, notoilet, dpiped, dwell, vincome.5. Click on the box to select

Enter Model.6. Click on

OK.

**INTERPRETATION:**

In this model, as before when **waz** was used, the results
have similar significance levels. Still, education and income are the strongest
influences on the outcome (p=0.000). The previous model using **waz **showed
a -0.321 point change in waz score for each unit decrease in education, and in this model
that corresponds to a 15% change in the prevalence of malnutrition.