Fit a multiple regression model relating CO2 product (y) to all seven regressors
ID: 3057854 • Letter: F
Question
Fit a multiple regression model relating CO2 product (y) to all seven regressors. What conclusion can you draw? a. call: 1m(formulay x1 x2 x3x4 x5 x6 x7, data -chem) Residuals: -20.035 -4.681 -1.144 4.072 Min 1Q Median 2 3Q Max 21.214 Coefficients: Estimate std. Error (Intercept) 53.937016 57.428952 -0.127653 0.281498 -0.229179 0.232643 0.824853 0.765271 -0.438222 0.358551 -0.001937 0.009654 0. 019886 0.008088 .993486 1. 089701 x2 x3 x4 t value Pr() (Intercept) 0.939 0.3594 -0.453 0.6553 -0.985 0.3370 1.078 0. 2946 -1.222 0.2366 -0.201 0.8431 x4 x6 2.459 0.0237* 1.829 0.0831 Signif. codes: 0 ‘ , 0.001 ‘*"' 0.01 ‘*, Residual standard error: 10.61 on 19 degrees of freedom Multiple R-squar ed: 0.728, Adjusted R-squared: 0. 6278 F-statistic: 7.264 on 7 and 19 DF, p-value: 0.0002674 3Explanation / Answer
Fit a multiple regression model relating CO2 product (y) to all seven regressors. What conclusion can you draw? a. call: 1m(formulay x1 x2 x3x4 x5 x6 x7, data -chem) Residuals: -20.035 -4.681 -1.144 4.072 Min 1Q Median 2 3Q Max 21.214 Coefficients: Estimate std. Error (Intercept) 53.937016 57.428952 -0.127653 0.281498 -0.229179 0.232643 0.824853 0.765271 -0.438222 0.358551 -0.001937 0.009654 0. 019886 0.008088 .993486 1. 089701 x2 x3 x4 t value Pr() (Intercept) 0.939 0.3594 -0.453 0.6553 -0.985 0.3370 1.078 0. 2946 -1.222 0.2366 -0.201 0.8431 x4 x6 2.459 0.0237* 1.829 0.0831 Signif. codes: 0 ‘ , 0.001 ‘*"' 0.01 ‘*, Residual standard error: 10.61 on 19 degrees of freedom Multiple R-squar ed: 0.728, Adjusted R-squared: 0. 6278 F-statistic: 7.264 on 7 and 19 DF, p-value: 0.0002674 3
Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.