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A College Admissions Counselor Was Interested in Finding Out How

question 7

Essay

A college admissions counselor was interested in finding out how well high school grade point averages (HS GPA) predict first-year college GPAs (FY GPA). A random sample of data from first-year students was reviewed to obtain high school and first-year college GPAs. The data are shown below: $\begin{array}{|l|l|l|l|l|l|l|l|l|}\hline \text {HS GPA }& 3.82 & 3.90 & 3.20 & 3.40 & 3.88 & 3.50 & 3.60 & 3.70 \\\hline \text {FY GPA} & 3.75 & 3.45 & 2.60 & 2.95 & 3.50 & 2.76 & 3.10 & 3.40 \\\hline\end{array}$

$\begin{array}{|c|c|c|c|c|c|c|c|c|}\hline\text { HS GPA} & 4.00 & 3.30 & 3.50 & 3.80 & 3.87 & 4.00 & 3.20 & 3.82 \\\hline\text { FY GPA }& 3.90 & 2.70 & 3.00 & 3.00 & 3.10 & 3.77 & 2.80 & 3.54 \\\hline\end{array}$

Dependent variable is: $\quad$ FY GPA
No Selector
$\mathrm{R}$ squared $=75.4 \% \quad \mathrm{R}$ squared (adjusted) $=73.6 \%$
$s=0.2118$ with $16-2=14$ degrees of freedom

$\begin{array}{llrrr}\text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 1.92283 & 1 & 1.92283 & 42.9 \\ \text { Residual } & 0.627867 & 14 & 0.044848 & \\ & & & & \\ \text { Variable } & \text { Coefticient } & \text { s.e. of Coeft } & \text { t-ratio } & \text { prob } \\ \text { Constant } & -1.56410 & 0.7306 & -2.14 & 0.0504 \\ \text { HS GPA } & 1.30527 & 0.1993 & 6.55 & \leq 0.0001\end{array}$

$A college admissions counselor was interested in finding out how well high school grade point averages (HS GPA) predict first-year college GPAs (FY GPA). A random sample of data from first-year students was reviewed to obtain high school and first-year college GPAs. The data are shown below: \begin{array}{|l|l|l|l|l|l|l|l|l|} \hline \text {HS GPA }& 3.82 & 3.90 & 3.20 & 3.40 & 3.88 & 3.50 & 3.60 & 3.70 \\ \hline \text {FY GPA} & 3.75 & 3.45 & 2.60 & 2.95 & 3.50 & 2.76 & 3.10 & 3.40 \\ \hline \end{array} \begin{array}{|c|c|c|c|c|c|c|c|c|} \hline\text { HS GPA} & 4.00 & 3.30 & 3.50 & 3.80 & 3.87 & 4.00 & 3.20 & 3.82 \\ \hline\text { FY GPA }& 3.90 & 2.70 & 3.00 & 3.00 & 3.10 & 3.77 & 2.80 & 3.54 \\ \hline \end{array} Dependent variable is: \quad FY GPA No Selector \mathrm{R} squared =75.4 \% \quad \mathrm{R} squared (adjusted) =73.6 \% s=0.2118 with 16-2=14 degrees of freedom \begin{array}{llrrr}\text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 1.92283 & 1 & 1.92283 & 42.9 \\ \text { Residual } & 0.627867 & 14 & 0.044848 & \\ & & & & \\ \text { Variable } & \text { Coefticient } & \text { s.e. of Coeft } & \text { t-ratio } & \text { prob } \\ \text { Constant } & -1.56410 & 0.7306 & -2.14 & 0.0504 \\ \text { HS GPA } & 1.30527 & 0.1993 & 6.55 & \leq 0.0001\end{array} -Create and interpret a 95% confidence interval for the slope of the regression line.$
-Create and interpret a 95% confidence interval for the slope of the regression line.

Definitions:

Manufacturing Cost Variance

The difference between the actual costs of production and the standard or expected costs, indicating efficiency or inefficiency in the manufacturing process.

Direct Materials Cost Variance

The difference between the actual cost of direct materials used in production and the standard cost expected to be used.

Direct Labor Cost Variance

The difference between the budgeted cost of direct labor and the actual cost incurred.

Cost Variance

The difference between the expected (budgeted) cost and the actual cost incurred.