DATA AND VIDEO TO WALK YOU THROUGH WILL BE ATTACHED BELOW
Here is data about heart disease. You will use it to predict who gets heart disease based on their sex and cholesterol numbers. The problem with this dataset is that sex and the presence/absence of heart disease are not labeled. You need to decide whether 0/1 are male or female. You then need to decide whether 0/1 for the heart disease variable means someone has heart disease. Once you figure it out, add text labels to make things clear.
First, set up a logistic regression model predicting heart disease using the main effects of sex and cholesterol. You know that males have higher heart disease than females. You also know that the greater someone’s cholesterol, the more likely they are to have heart disease. Look at the estimated marginal means plot to figure out what the 0/1 values mean for the sex and heart disease variables. Once you do that, proceed to the quiz and answer the questions.
Note. These checks and fixes are very important when working with a new dataset. This sort of data inspection is very typical.
1. What is the Chi-squared overall model test when only including the main effects in the model? (Answer as X.XX)
4. Assess the collinearity of the predictors in model 1 (only main effects in the model). What is the variance inflation factor (VIF) for sex? (Answer as X.XX)
8. Use Model 1. What is the probability that a male with cholesterol of 400 will have heart disease? (Answer as X.XXX)