So, how do we interpret this? Carefully, in a word. Review pages 281 and 282 for how the probability is determined.) This suggests also that there is a negative impact on willingness to pay for an online news experience if the person views In-Depth Analysis of National Stories as very impactful in the decision. When B is negative, it means that increasing that X will decrease the LN of the odds (Y∗c), which will, in turn, decrease the odds, which, in turn, will decrease the probability of willingness to pay. You’ll notice the “national” coefficient is significant ( p-value = 0.033) and is negative! (Remember, column B displays the slope of the linear equation, when the Y∗c is the LN (natural log) of the odds. The final noteworthy point is that the stepwise results have thrown us an interesting curve. Our enthusiasm for this predictor is still extremely high. Obviously, this variable is still a very strong predictor. However, the p-value has remained 0.000 (which, we recall means 0.000 to three digits). It’s gone down from 17.7 to 10.7 (rounded). (If we forced it to enter as a sixth variable, its p-value would be 0.113, which is, of course, not less than 0.05, which is why it did not enter still, 0.113 is not considered that much above 0.05.)Īnother difference between the binary logistic multiple regression and binary logistic stepwise regression results is the difference of the coefficient for the “Subscriber to Printed Newspaper” variable.

As a consequence, different p-values and different benchmarks have to be overcome to be significant at 0.05. What do we make of that? Well, regression analysis (linear or logistic) is a complex technique, with different patterns of overlap when different sets of variables are in the equation. But there are major differences in the output.įirst, the Unique Local Content Not Available on a Local Newspaper Site has dropped out of the regression model. In step 5, there are still five variables, same as in the earlier multiple regression. (To brush up on stepwise regression, refer back to Chapter 10.) As we noted in discussing stepwise regression in Chapter 10, where it was first introduced, it is only the last step that really matters. You can see in the third (bottom) section that there were five steps. Let’s begin with the “Variables in the Equation” section at the bottom of the output. Stepwise (Forward: LR) regression output SPSS with.

Stepwise regression excel for mac