[ Y= ext { Calories and } X_{1}=F ext { at }, X_{2}= ext { Protein and } X_{3}= ext { Fiber. } ] Regression Equation Calories ( quad 85.01+18.56 ) Fat +7.54 Protein +3.51 Fiber Model Summary egin{tabular}{llll} S & R-sq & R-sq(adj) & R-sq(pred) \ 21.4164 & ( 66.07 % ) & ( 62.16 % ) & ( 42.39 % ) end{tabular} Which ( x ) variable has the most influence in this multiple linear relationship? In other words, which of these three x variables is the best for predicting ( y ) ? [ egin{array}{l} x_{3}= ext { Fiber } \ x_{2}= ext { Protein } \ x_{1}= ext { Fat } end{array} ]

Transcribed Image Text:

We have a dataset that shows nutritional information for 30 different breakfast cenah Here in computer output for a four variable relationship between Y-Calories and X-Fat X-Protein and X-Fiber Regression Equation Calories Model Summary $ 85.01+1856 Fat+754 Protein 351 Fiber R-4 R-sqla R-sqlpred) 21.4164 66.07% 62.16% 42.39% Coefficients Term Coef SE Coef T-Value P-Value Constant 8501 8.33 10.18 0.000 Fat 1856 371 500 0.000 105 Prote 754 305 247 0020 Fiber 351 246 1.42 0.166 136 135 Which a variable has the most influence in this multiple linear relationship? to other words, which of these threex variables is the best for predicting y X-Fiber X-Protein OX-Fat We have a dataset that shows nutritional information for 30 different breakfast cenah Here in computer output for a four variable relationship between Y-Calories and X-Fat X-Protein and X-Fiber Regression Equation Calories Model Summary $ 85.01+1856 Fat+754 Protein 351 Fiber R-4 R-sqla R-sqlpred) 21.4164 66.07% 62.16% 42.39% Coefficients Term Coef SE Coef T-Value P-Value Constant 8501 8.33 10.18 0.000 Fat 1856 371 500 0.000 105 Prote 754 305 247 0020 Fiber 351 246 1.42 0.166 136 135 Which a variable has the most influence in this multiple linear relationship? to other words, which of these threex variables is the best for predicting y X-Fiber X-Protein OX-Fat