A MATH 202 Statistics student is looking for a house to rent in the ekmeky area. He saw 21 rental houses with a net area of 50 square meters to 80 square meters, and the following question about the prices of these houses was stuck in his mind. There does not seem to be much of a relationship between house prices and the net area of the house. For this reason, he made a regression analysis to understand the relationship between the net areas of the houses (in terms of square meters) and the house prices (in terms of TL) and obtained the following excel output file: SUMMARY Regression Statistics Multiple R R Square Adjusted Square Standard Error 1068.990 Observations 21 0.673 0.453 0.424 ANOVA Regression Residual Total Intercept Areas df 1 19 20 Coefficients -1142.588 102.875 SS 17977371.96 17977371.96 21712056.61 1142739.822 39689428.57 Standard Error 1719.096 25.937 MS a) Write dependent and independent variables. b) Write the regression equation. t Stat -0.665 3.966 F 15.73181543 P-value 0.514 0.001 c) How would you best explain the y-intercept within the range of the analysis? d) How would you best explain the slope coefficient? e) What would be the estimated rent of a 65 square meters house? What would be the estimated rent of a 90 square meters house? f) What would be the best explanation of R Square? g) Test whether there is a linear relation between the net area of houses and the price of houses at the a = 0.05 significance level. Write your hypotheses. Write the statistical implication of the result you found. h) Construct a 95% confidence interval for the correct slope coefficient. Write the statistical implication of the result you found.