Can you check my answers

A real estate agency collects the data in Table 15.4 concerning y= sales price of a house (in thousands of dollars) I1 = home size (in hundreds of square feet) I2 = rating (an overall "niceness rating" for the house expressed on a scale from 1 [worst] to 10 [best], and provided by the real estate agency) The agency wishes to develop a regression model that can be used to predict the sales prices of future houses it will list. Use Excel, JMP, or Minitab to perform a regression analysis of the real estate sales price data. Use the output to do (1) through (4) for each of 69. 61. and 62. (1) Find by. "by . and the statistic for testing He: 6, = @ on the output and report their values. (Round your answers to by, by to 4 decimal places. Round t answers to 3 decimal places.) bo = 20.3468 , =(60) = 4.8914 , 10 = 5.000 b1 = 5.6128 , =(61) = 0.2285 , 11 = 24.561 b2 = 3.8344 . =(62) = 0.4332 , 12 = 8.851 (2) Using the t statistic and appropriate critical values, test Ny: 6, = @ versus My : 6, = @ by setting @ equal to .05. Which independent variables are significantly related to y in the model with a = .05? We HO: 80 = 0 with a = .05 and conclude that the intercept significant at the .05 level We conclude that the independent variable x1 (home size) significant at the . 05 level of significance We conclude that the independent variable x2 (niceness rating) significant at the .05 level of significance. (3) Find the p-value for testing My: 8, = @ versus Hy: 6, # @ on the output. Using the p-value, determine whether we can reject He by setting a equal to 10, 05. .01, and .001. What do you conclude about the significance of the independent variables in the model? We can conclude that the intercept 80 significant at the . 10, .05.01 and .001 levels of significance. We conclude that the independent variable x1 (home size) significant at the . 10. .05, .01, and .001 levels of significance. We conclude that the independent variable x2 (niceness rating) is significant at the . 10. .05, .01, and .001 levels of significance.TABLE 15.4 The Real Estate Sales Price Data DS RealEst2 X2 180 23 5 98.1 11 173.1 20 136.5 17 141 15 165.9 21 193.5 24 127.8 13 163.5 19 172.5 25 Source: R. L. Andrews and J. T. Ferguson, "Integrating Judgement with a Regression Appraisal," The Real Estate Appraiser and Analyst 52, no. 2 (1986)