SAS output from fitting three regression models to the data below is given in Ta
ID: 3355913 • Letter: S
Question
SAS output from fitting three regression models to the data below is given in Table 3 Table 3: SAS Output Sum of Squares 3004.41465 67.58535 3072.00000 Mean Square 3004.41465 16.89634 DF Source Model F ValuePr> F 177.81 0.0002 283 49 rror Corrected Total 55 8 31 75 16 7 Root MSE Dependent Mean 0.9780 54.00000 Adj R-Sq 0.9725 .11052 R-Sqare Standarod a)For each regression, what are your conclusions? (b)Briefly disuss the orll signiicance, quality-of-fit of the regression, and the Parameter Estimate 20.23610 3.43362 Errot Value Pr> Itl ariable Intercept 3.03764 0.25749 0.0026 0.0002 significanee of the explanatory variable(s) the regression model. 13.33 DF Sum of Squar Source Model Error Corrected Total F Value 2913.36198 158.63802 3072.00000 es Mean Square 2913.36198 39.65950 73.46 0.0010 Root MSE Dependent Mean 6.29758 -Sare 0.9484 54.00000 Adj R-Sq 0.9355 Parameter Estimate 91.17851 -1.34380 Standard Error 5·04245 0.15679 Variable Intercept Value P > Itl F 0.0029 4.54876 R-Square Root MSE Dependent Mean 0.9798 54.00000 Adj R-Sq 0.9663 Standarod Parameter Estimate 33.93210 2.78476 -0.26442 ariable Intercept 26.74829 1.28906 0.51232 Errot Value Pr> Itl 0.2941 0.1195 0.6414 X2 -0.52Explanation / Answer
Here using SAS gives Three model .
a) and b )
Model 1
Y = 20.23 + 3.43 *X1
Model 1 is very good fit because R-square of this model is 0.9725 and variable X1 is Significant
because P-value is less than 0.05 that's why model one very good .
Model 2 .
Y= 91.17 -1.34 *X2
Model 2 is very good fit because R-square of this model is 0.9355 and variable X2 is Significant
because P-value is less than 0.05 that's why model one very good .
Model 3
Y=33.92 +2.78*X1 -0.26*X2
Model 3 is good fit because R-square of this model is 0.9355 and variable X1 is Significant
because P-value is less than 0.05 and variable X2 is not Significant because P-value is greater than 0.05
that's why model 3 on not very good .
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