Run a regression with price as the response/dependent (Y) variable and all the other variables as independent/predictor (X) variables. a. Using just one number from each of your outputs, compare the regression from problem 1 to this new regression. Which do you think explains home prices better and why, and which number did you use for this comparison? b. According to the coefficients from this new regression, what is the equation you would use to predict the price of a home? c. If you find two houses that differ only in that one of them has an additional floor (that amounts to 1200 extra square feet in size), with one more bedroom and two more bathrooms as well, what is the expected difference in price (in dollars) between these two houses according to this regression?SUMMARY OUTPUT Regression Statistics Multiple R 0.77789293 R Square 0.60511741 Adjusted R Sq 0.5790812 Standard Erro 146.266766 Observations 98 ANOVA of MS F Significance F Regression 6 2983351.16 497225.193 23.2413744 1.8125E-16 Residual 91 1946850.98 21393.9668 Total 97 4930202.14 Coefficients itandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -69.271221 80.5308089 -0.8601829 0.39194882 -229.23578 90.6933378 -229.23578 90.6933378 bath 46.7720149 24.0316767 1.94626515 0.05470793 -0.9639587 94.5079886 -0.9639587 94.5079886 area 0.10573473 0.02355787 4.48829742 2.0964E-05 0.05893992 0.15252955 0.05893992 0.15252955 lot 330.750364 74.5029691 4.43942528 2.5277E-05 182.759369 478.74136 182.759369 478.74136 Central_cooli 88.3546274 30.4257511 2.90394236 0.00462415 27.9176105 148.791644 27.9176105 148.791644 bed -12.403365 26.1271703 -0.4747305 0.6361164 -64.301779 39.4950496 -64.301779 39.4950496 Heat_pump 39.5217054 39.8992904 0.99053655 0.32453847 -39.733334 118.776744 -39.733334 118.776744