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 = β_{0} +
β_{1}x_{1} +
β_{2}x_{2} +
β_{3}x_{3} +
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 (2) Report R2 and R¯¯¯2 as shown on the output. (Round your answers to 4 decimal places.) R^2 0.9563 (3) Report SSE, s2, and s as shown on the output. (Round your answers to 4 decimal places.) SSE (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? pvalue =.0000. Since this pvalue is less than α =.001, we
have(extremly strong,no,some,strong,very strong) 
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