The Likelihood Ratio (LR) Test to Assess the Fit of a Logistic Regression

Based on the discussion between Racicot and Lavoie about regression assumptions, whose comments are _most likely_ accurate?

Incorrect. Racicot isn't accurate in stating that the error term doesn't need to exhibit any specific distribution. An assumption inherent in linear regression analysis is that the error term is normally distributed.

Incorrect. Lavoie isn't accurate in stating that the error term must have an expected value other than zero. An assumption inherent in linear regression analysis is that the expected value of the error term is zero.

That's right. Neither Racicot nor Lavoie are completely accurate. The accuracy of a multiple regression depends on error terms being normally distributed with an expected value of zero. Lavoie, however, states that the error term must have an expected value other than zero. And Racicot states that the error term doesn't need to exhibit any specific distribution.

Racicot's but not Lavoie's

Lavoie's but not Racicot's

Neither Racicot's nor Lavoie's