We give excel output of regression analysis. above output we give the regression model and the number of observations, n, used to perform the regression analysis under consideration. using the model, sample size n, and output: model: y = β0 + β1x1 + β2x2 + β3x3 + ε sample size: n = 30 regression statistics multiple r .1761 r square .0310 adjusted r square . standard error .6970 observations 30 anova df ss ms f p regression 3 .3992 .1331 .2741 .8435 residual 26 12.6203 .4854 total 29 13.0195 (1) report sse, s2, and s as shown on the output. (round your answers to 4 decimal places.) sse s2 s (2) report r2 and r⎯⎯⎯2as shown on the output. (round your answers to 4 decimal places.) r2 picture (3) report the total variation, unexplained variation, and explained variation as shown on the output. (round your answers to 4 decimal places.) total variation unexplained variation explained variation (4) calculate the f(model) statistic by using the explained variation, the unexplained variation, and other relevant quantities. (round your answer to 2 decimal places.) f(model) (5) use the f(model) statistic and the appropriate critical value to test the significance of the linear regression model under consideration by setting α equal to .05. h0: β1 = β2 = β3 = 0 by setting α = .05. (6) use the f(model) statistic and the appropriate critical value to test the significance of the linear regression model under consideration by setting α equal to .01. h0: β1 = β2 = β3 = 0 by setting α = .01. (7) find the p−value related to f(model) on the output. using the p−value, test the significance of the linear regression model by setting α = .10, .05, .01, and .001. what do you conclude? since this p-value is greater than α = .10, .05, .01, and .001, we have evidence that h0: β1 = β2 = β3 = 0 is false. that is, we have evidence that at least one of x1, x2 , x3issignificantly related to y.