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.

Which Statement Correctly Compares T-Distributions to the Normal Distribution

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