Suppose we define activity level as the number of calories burned in a single workout. We define a multiple linear regression model as E(Y X) = Bo + Biage + B21(sex = male) + 831(sex = male) * ageE(YIX) = Bo + Bjage + 821(sex = male) + 831(sex = male) * age where the third term is an interaction term between these two predictors. Considering this model, . What does this interaction term do to the relationship between age, sex and activity level? How do we interpret all the parameters in this model? . How would the interpretation of the coefficients change if instead we only fit the model E(Y| X) = Bo + Biage + B21(sex = male) * age E(Y X) = Bo + Bjage + B21(sex = male) * age? . If the true relationship in the population was actually E(Y X) = Bo + Biage + B21(sex = male) + 831(sex = male) * age + 84 (length of workout) E(Y X) = Bo+ Bjage + B21(sex = male) + 831(sex = male) * age + B4(length of workout), what does this mean for the statistical properties of the estimated coefficients? What about whether assumptions are satisfied? . Suppose we had instead measured activity level as either being high (Y=1) or low (Y=0). Will each of our assumptions in linear regression be satisfied? Why or why not