Simple Linear Regression Can you please assist with this with a detailed analysis? I am interested in examining the factors that affect the selling price of a house. We collect data for sample of 27 houses. For each house we record the \"Selling price\" and the \"Size of house\". The variable \"Selling price\" was recorded in \"thousands of euros\" (i.e. 1 unit: 1000), while the variable \"Size of house\" was recorded in square meters (i.e. 1 unit: 1 square meter, m2). Please notice that in SPSS the decimal point is denoted by the dot (.), e.g. 8.206 is around 8). I have the following output from SPS S. Model Summary Std. Error of the R Square Adjusted R Square Estimate I_113-04157 ANOVA moie Sum or Squares es_ 1 Regression 860535.066 860535.066 67.343 Residual 319459.897 12778396 Total 1 179994.963 Coefficients Standardized Unstandard ized Coefficients Coefficients E_Std- Error t 1 (Constant) 389.425 1.110 .278 Size of house .048 8.206 .000 Can you please assist with the following questions? (1) Do the results show that the bigger the size of the house, the higher its selling price? We use a 5% level of significance. (2) Can you please write the estimated regression model and interpret the coefficients? (3) Can you please write the value of the coefficient of determination and interpret this value. Do we have a good model fit to the data? (4) What is the value of the correlation coefcient between the size of the house and the selling price? Can you Interpret this value. (5) Using this model can we make a forecast of the selling price of a house that has size 200 square meters. If you can assist, I would much appreciate it