BAX History; x Promotion; + BXlogIncome; x Promotion; + Eij Copy and paste the results here. 3). Estimate the following hierarchical linear model using the function Imer( ) in the R package "Ime4" logSowRation = Boi + Bix Balance; + BrixPromotion;; + &j Boi = Mo + M1 X History; + Si Bzi = No + 71 X History; + 12xlogIncome; + Si Following what we did in our class, please rewrite this hierarchical linear model as a one- level linear regression model with random effects. Which variables (and interactions) in the regression have fixed effects? Which ones have random effects? Specify the variables in Imer() and run the regression (please specify REML=F, control=ImerControl(optimizer ="Nelder_Mead") in Imer()). Please copy and paste the summary() of the regression here. Please interpret the estimated fixed effects in the regression. Compare model fit using AIC() and BIC() with the model in (2)