In estimating the linear probability model for whether an individual clicked through on an advertisement based off how much time the individual spent on the website, the regression results are as follows: ClickThrough_i = 0.4(0.08) + 0.07(0.01)TimeSpent_i, where the standard errors of each coefficient are reported in parenthesis. Why would the fact that someone in the sample was on the website for 10 hours be problematic? A) It suggests the outcome is measured with error. B) It suggests that you have a non-representative sample. C) The prediction for that individual will fall above 1 (the maximum of ClickThrough). D) It suggests that the errors are heteroscedastic.

Question

Quizplus · Accepted Answer

The regression equation predicts that the probability of clickthrough for an individual who spent 10 hours on the website would be ClickThrough_i = 0.4(0.08) + 0.07(0.01)(10) = 0.032, which is greater than 1 (the maximum possible value for a probability). This suggests that the model is not appropriate for predicting outcomes for individuals who spent more than a certain amount of time on the website and may need to be re-specified or truncated.

In Estimating the Linear Probability Model for Whether an Individual