Write the best-fit linear model for the data.
-The paired data below consist of the test scores of 6 randomly selected students and the number of hours they studied for the test. Find a linear function that approximates a student's score as a function of the number of hours he or she
Studied. $\begin{array}{l|rrrrrr}
\text { Hours } & 5 & 10 & 4 & 6 & 10 & 9 \
\hline \text { Score } & 64 & 86 & 69 & 86 & 59 & 87
\end{array}$

A) $y = - 67.3 + 1.07 x$
B) $y = 33.7 - 2.14 x$
C) $y = 33.7 + 2.14 x$
D) $y = 67.3 + 1.07 x$

Question

Write the best-fit linear model for the data.
-The paired data below consist of the test scores of 6 randomly selected students and the number of hours they studied for the test. Find a linear function that approximates a student's score as a function of the number of hours he or she
Studied. $\begin{array}{l|rrrrrr}
\text { Hours } & 5 & 10 & 4 & 6 & 10 & 9 \
\hline \text { Score } & 64 & 86 & 69 & 86 & 59 & 87
\end{array}$

A) $y = - 67.3 + 1.07 x$
B) $y = 33.7 - 2.14 x$
C) $y = 33.7 + 2.14 x$
D) $y = 67.3 + 1.07 x$

Quizplus · Accepted Answer

The best-fit linear model is found using linear regression, which minimizes the sum of the squared differences between the observed values and the values predicted by the model. The correct model, $y = 67.3 + 1.07x$, suggests that for each additional hour studied, the expected score increases by approximately 1.07 points, starting from a base score of 67.3 when no hours are studied. This model is chosen based on statistical calculations that are not shown here but involve analyzing the given data points to find the line of best fit.

Write the Best-Fit Linear Model for the Data Hours 51046109 Score 648669865987\begin{array}{l|rrrrrr}\text { Hours } & 5 & 10 & 4 & 6 & 10 & 9 \\\hline \text { Score } & 64 & 86 & 69 & 86 & 59 & 87\end{array} Hours Score ​564​1086​469​686​1059​987​​

Write the Best-Fit Linear Model for the Data $\begin{array}{l|rrrrrr}\text { Hours } & 5 & 10 & 4 & 6 & 10 & 9 \\\hline \text { Score } & 64 & 86 & 69 & 86 & 59 & 87\end{array}$