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

question 82

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Professor
A statistics professor investigated some of the factors that affect an individual student's final grade in his 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 } + \epsilon where:
y = final mark (out of 100)
x1 = number of lectures skipped
x2 = number of late assignments
x3 = 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:
y^=41.63.18x11.17x2+.63x3\hat { y } = 41.6 - 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}{l|lll|}\hline \text { Predictor } & \text { Coef } & \text { StDev } & 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=13.74S = 13.74
RSq=30.0%\mathrm { R } - \mathrm { Sq } = 30.0 \%
Analysis of Variance
 Source of Variation  df  SS MSF Regression 337161238.6676.558 Error 468688188.870 Total 4912404\begin{array}{l|llll}\hline \text { Source of Variation } & \text { df } & \text { SS } & M S & F \\\hline \text { Regression } & 3 & 3716 & 1238.667 & 6.558 \\\text { Error } & 46 & 8688 & 188.870 & \\\hline \text { Total } & 49 & 12404 & & \\\hline\end{array}
-Do these data provide enough evidence to conclude at the 5% significance level that the final mark and the number of skipped lectures are linearly related?
Test statistic = ____________________
Critical Value = ____________________
Conclusion: ____________________

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