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A Statistics Professor Investigated Some of the Factors That Affect y=β0+β1x1+β2x2+β3x3+εy = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \varepsilon

question 69

Essay

A statistics professor investigated some of the factors that affect an individual student's final grade in his or her course. He proposed the multiple regression model: y=β0+β1x1+β2x2+β3x3+εy = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \varepsilon .
Where:
y = final mark (out of 100). x1x _ { 1 } = number of lectures skipped. x2x _ { 2 } = number of late assignments. x3x _ { 3 } = mid-term test mark (out of 100).
The professor recorded the data for 50 randomly selected students. The computer output is shown below.
THE REGRESSION EQUATION IS  A statistics professor investigated some of the factors that affect an individual student's final grade in his or her course. He proposed the multiple regression model:  y = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \varepsilon  . Where: y = final mark (out of 100).  x _ { 1 }  = number of lectures skipped.  x _ { 2 }  = number of late assignments.  x _ { 3 }  = mid-term test mark (out of 100). The professor recorded the data for 50 randomly selected students. The computer output is shown below. THE REGRESSION EQUATION IS   =  41.6 - 3.18 x _ { 1 } - 1.17 x _ { 2 } + .63 x _ { 3 }   \begin{array} { | c | c c c | }  \hline \text { Predictor } & \text { Coef } & \text { StDev } & \mathrm { T } \\ \hline \text { Constant } & 41.6 & 17.8 & 2.337 \\ x _ { 1 } & - 3.18 & 1.66 & - 1.916 \\ x _ { 2 } & - 1.17 & 1.13 & - 1.035 \\ x _ { 3 } & 0.63 & 0.13 & 4.846 \\ \hline \end{array}  S = 13.74 R-Sq = 30.0%.  \begin{array}{l} \text { ANALYSIS OF VARIANCE }\\ \begin{array} { | l | c c c c | }  \hline \text { Source of Variation } & \mathrm { df } & \text { SS } & \text { MS } & \text { F } \\ \hline \text { Regression } & 3 & 3716 & 1238.667 & 6.558 \\ \text { Error } & 46 & 8688 & 188.870 & \\ \hline \text { Total } & 49 & 12404 & & \\ \hline \end{array} \end{array}  Do these data provide enough evidence to conclude at the 5% significance level that the model is useful in predicting the final mark? = 41.63.18x11.17x2+.63x341.6 - 3.18 x _ { 1 } - 1.17 x _ { 2 } + .63 x _ { 3 }  Predictor  Coef  StDev T Constant 41.617.82.337x13.181.661.916x21.171.131.035x30.630.134.846\begin{array} { | c | c c c | } \hline \text { Predictor } & \text { Coef } & \text { StDev } & \mathrm { T } \\\hline \text { Constant } & 41.6 & 17.8 & 2.337 \\x _ { 1 } & - 3.18 & 1.66 & - 1.916 \\x _ { 2 } & - 1.17 & 1.13 & - 1.035 \\x _ { 3 } & 0.63 & 0.13 & 4.846 \\\hline\end{array} S = 13.74 R-Sq = 30.0%.  ANALYSIS OF VARIANCE  Source of Variation df SS  MS  F  Regression 337161238.6676.558 Error 468688188.870 Total 4912404\begin{array}{l}\text { ANALYSIS OF VARIANCE }\\\begin{array} { | l | c c c c | } \hline \text { Source of Variation } & \mathrm { df } & \text { SS } & \text { MS } & \text { F } \\\hline \text { Regression } & 3 & 3716 & 1238.667 & 6.558 \\\text { Error } & 46 & 8688 & 188.870 & \\\hline \text { Total } & 49 & 12404 & & \\\hline\end{array}\end{array} Do these data provide enough evidence to conclude at the 5% significance level that the model is useful in predicting the final mark?

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