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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 43

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|ccc|} \hline \text { Predictor } & \text { Coef } & \text { StDev } & \text { 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}   \mathrm { S } = 13.74 \quad \mathrm { R } - \mathrm { Sq } = 30.0 \%  ANALYSIS OF VARIANCE  \begin{array}{|l|cccc|} \hline \text { Source of Variation } & \mathrm{df} & \mathrm{SS} & \mathrm{MS} & \mathrm{F} \\ \hline \text { Regression } & 3 & 3716 & 1238.667 & 6.558 \\ \text { Error } & 46 & 8688 & 188.870 & \\ \hline \text { Total } & 49 & 12404 & & \\ \hline \end{array}   What is the coefficient of determination? What does this statistic tell you? =41.63.18x11.17x2+.63x3 = 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}{|c|ccc|}\hline \text { Predictor } & \text { Coef } & \text { StDev } & \text { 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=13.74RSq=30.0%\mathrm { S } = 13.74 \quad \mathrm { R } - \mathrm { Sq } = 30.0 \%
ANALYSIS OF VARIANCE
 Source of Variation dfSSMSF Regression 337161238.6676.558 Error 468688188.870 Total 4912404\begin{array}{|l|cccc|}\hline \text { Source of Variation } & \mathrm{df} & \mathrm{SS} & \mathrm{MS} & \mathrm{F} \\\hline \text { Regression } & 3 & 3716 & 1238.667 & 6.558 \\\text { Error } & 46 & 8688 & 188.870 & \\\hline \text { Total } & 49 & 12404 & & \\\hline\end{array}
What is the coefficient of determination? What does this statistic tell you?


Definitions:

Null Hypothesis

A hypothesis that suggests there is no statistical significance between the two observed phenomena.

H Statistic

A measure of statistical significance in the Kruskal-Wallis H test, used to determine if there are differences between two or more groups of an independent variable on a continuous or ordinal dependent variable.

Wilcoxon Signed Rank Test

A non-parametric statistical test used to compare two related samples or repeated measurements on a single sample to assess whether their population mean ranks differ.

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The amount or level of aluminum present in a given substance, mixture, or environment.

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