Where Y=sale price (in $1000's) Xl=oor(1=rst oor, 2=second oor, ...) X2=distance from elevator (in number of condo units) X3=ocean View (0=no, 1=yes) X4=end unit (0=no, 1=yes) X5=furnished (0=no, 1=yes) The data file condo.txt can be downloaded from mycourses/exam. The fix columns in this data set are, in order, Sale Prices, Floor, Elevator Distance, Ocean View, End Unit, and Furnished. (You might choose Minitab, R, SAS, Python or other software packages for the data analysis) 1) State the model and assumptions. 2) Use scatter plot matrices and correlation matrices to examine the data for linear relationships. 3) Test for a regression relationship. Evaluate the usefulness of the model. 4) Test the individual parameters. 5) Is there multicollinearity in the full model? Explain. 6) Use stepwise and best subsets techniques to reduce the model. 7) Is there multicollinearity in the reduced model? Explain. 8) Using the reduced model, examine the data for outliers and inuential observations. 9) Check the assumptions for the reduced model. 10) If there is a problem with the reduced model, propose a solution. (You don't have to do anything, just make suggestions.)