(Solved): Suppose after running the regression Y=1+2X2+3X3+4X4+5X5 and ...

Suppose after running the regression $Y={?}_1+{?}_2{X}_2+{?}_3{X}_3+{?}_4{X}_4+{?}_5{X}_5$ and examining a plot of the residuals, you have reason to suspect that there is heteroscedasticity in your model. Assume that the standard error of the entire regression model ${?}_{ui}$ is directly proportional to the value of the independent variable ${\mathrm{X}}_{2\mathrm{i}}$. Mathematically, the relationship looks like ${?}_{ui}$ $=?X_{2i}$ How might you transform the variables in the regression model to correct for this heteroscedasticity? A. None of these answers are correct. B. Multiply each variable by the weight $=1/\sqrt{X_{2i}}$ and estimate the transformed regression without a constant. C. Multiply each variable by the weight $=1/X_{2i}$ and estimate the transformed regression with a constant. D. Multiply each variable by the weight $=1/?$ and estimate the transformed regression with a constant. Reset Selection Mark for Review What's This?