Use the regression feature of a graphing utility to find a quadratic model for the data below. $\begin{array}{|r|c|}
\hline \boldsymbol{x} & \boldsymbol{y} \
\hline-2 & 9.8 \
\hline-1 & 4.1 \
\hline 0 & 3.3 \
\hline 1 & 6.6 \
\hline 2 & 13.8 \
\hline 3 & 24.1 \
\hline 4 & 39.5 \
\hline
\end{array}$

A) $y = 1.91 x ^ { 2 } + 1.06 x + 3.15$
B) $y = 1.81 x ^ { 2 } + 1.02 x + 3.3$
C) $y = 2.21 x ^ { 2 } + 0.92 x + 3.3$
D) $y = 2.11 x ^ { 2 } + 0.87 x + 3.65$
E) $y = 2.01 x ^ { 2 } + 0.97 x + 3.44$

Question

Use the regression feature of a graphing utility to find a quadratic model for the data below. $\begin{array}{|r|c|}
\hline \boldsymbol{x} & \boldsymbol{y} \
\hline-2 & 9.8 \
\hline-1 & 4.1 \
\hline 0 & 3.3 \
\hline 1 & 6.6 \
\hline 2 & 13.8 \
\hline 3 & 24.1 \
\hline 4 & 39.5 \
\hline
\end{array}$

A) $y = 1.91 x ^ { 2 } + 1.06 x + 3.15$
B) $y = 1.81 x ^ { 2 } + 1.02 x + 3.3$
C) $y = 2.21 x ^ { 2 } + 0.92 x + 3.3$
D) $y = 2.11 x ^ { 2 } + 0.87 x + 3.65$
E) $y = 2.01 x ^ { 2 } + 0.97 x + 3.44$

Quizplus · Accepted Answer

The quadratic regression model that best fits the given data is $y = 2.01 x ^ { 2 } + 0.97 x + 3.44$, as it minimizes the sum of squared differences between the observed and predicted values.

Use the Regression Feature of a Graphing Utility to Find