Question

# We give JMP output of regression analysis. Above output we give the regression model and the...

We give JMP 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

 Summary of Fit RSquare 0.956255 RSquare Adj 0.951207 Root Mean Square Error 0.240340 Mean of Response 8.382667 Observations (or Sum Wgts) 30
 Analysis of Variance Source df Sum of Squares Mean Square F Ratio Model 3 32.829545 10.94320 189.4492 Error 26 1.501842 0.05780 Prob > F C. Total 29 34.331387 <.0001* (1) 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 (2) Report R2 and R¯¯¯2 as shown on the output. (Round your answers to 4 decimal places.) R^2   0.9563 R̅^2   0.9512   (3) Report SSE, s2, and s as shown on the output. (Round your answers to 4 decimal places.) SSE   s^2 (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)       (6) 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? p-value =.0000. Since this p-value is less than α =.001, we have(extremly strong,no,some,strong,very strong) evidence that H0: β1 = β2 = β3 =0 is false. That is, we have (extremly strong,no,some,strong,very strong) evidence that at least one of x1, x2 ,and x3is significantly related to y.

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