Market value by land, age, sq ft Regression Statistics Multiple R 0.9178 R Square 0.8424 Adjusted R Square 0.8242 Standard Error 60.5007 Observations 30 ANOVA di SS MS F Significance F Regression 3 508789.5253 169596.5084 46.3336 0.0000 Residual 26 95168.7217 3660.3355 Total 29 603958.2470 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95% Upper 95% Intercept 260.6791 66.3288 3.9301 0.0006 124.3383 397.0200 124.3383 397.0200 Property Size (acres 362.8318 48.6233 7.4621 0.0000 262.8851 462.7784 262.8851 462.7784 Age -1.7543 0.5483 -3.1996 0.0036 -2.8813 -0.6273 -2.8813 -0.6273 House Size (square 0.1109 0.0228 4.8682 0.0000 0.0641 0.1577 0.0641 0.1577Nassau County is located approximately 25 miles east of New York City. The data organized and stored in GLENCOVE include the fair market value (in $thousands), land area of the property in acres, and age, in years, for a sample of 30 single-family homes located in Glen Cove, a small city in Nassau County. You eveloped a multiple linear regression model to predict the fair market value based on land area of the property (in acres), and age, in years; and square ft of house. See the attached file GlenCov MR info.docx a. What are the null and alternative hypotheses? b. State the multiple regression equation. c. Interpret the meaning of the slopes and if you consider them valid and why. d. And in this problem, explain why the intercept coefficient, has no practical meaning in the context of this problem. e. Predict the mean fair market value for a house that has a land area of 0.3 acre, is 55 years old and has 2,500 sq.ft. of dwelling. f. Construct a 95% condence interval estimate for the mean fair market value for houses that has a land area of 0.3 acre, is 55 years old and has 2,500 sq.ft. of dwelling. To simplify, only vary the house size coefficient