1, Linear regression (20 #)) Simple linear regression is a useful approach for predicting a response on the basis of a single predictor variable and takes the form y = /, + fix+ & . Suppose a data set containing 3 observations {(x,y.): (1, 5), (0. 3), (-1, -2); and the least squares coefficient estimates are 2 for po and 3 for /, on the basis of the data set. Please calculate the R statistic based on the estimates and analyze how much proportion of variability in response y can be explained using x? Note that R' = TSS - RSS , where the total swan of squares TSS- ) (y, - P) , the sum of squared TSS residuals RSS=)(y, - v.)' and n is the number of the observations (in this case , 3)