The bigger the stop sign, the more expensive it is. Here is a graph of the height of a sign in inches versus its cost in dollars.

To achieve linearity, the data was transformed using a square root function of cost. Here are the results and a residual plot.
Dependent Variable: sqrt(cost)
$ \mathrm{R} $ (correlation coefficient) $ =0.98946627 $
$ R-s q=0.97904349 $
s: 0.2141

$\begin{array}{lrr}
\text { Parameter } & \text { coeff } & \text { se } \
\text { Intercept } & 1.1857 & 0.4346 \
\text { height } & 0.1792 & 0.0151
\end{array}$

-Do you think this transformation for linearity was successful? Why?

Question

The bigger the stop sign, the more expensive it is. Here is a graph of the height of a sign in inches versus its cost in dollars.

To achieve linearity, the data was transformed using a square root function of cost. Here are the results and a residual plot.
Dependent Variable: sqrt(cost)
$ \mathrm{R} $ (correlation coefficient) $ =0.98946627 $
$ R-s q=0.97904349 $
s: 0.2141

$\begin{array}{lrr}
\text { Parameter } & \text { coeff } & \text { se } \
\text { Intercept } & 1.1857 & 0.4346 \
\text { height } & 0.1792 & 0.0151
\end{array}$

-Do you think this transformation for linearity was successful? Why?

Quizplus · Accepted Answer

The Answer of The bigger the stop sign, the more expensive it is. Here is a graph of the height of a sign in inches versus its cost in dollars.

To achieve linearity, the data was transformed using a square root function of cost. Here are the results and a residual plot.
Dependent Variable: sqrt(cost)
$ \mathrm{R} $ (correlation coefficient) $ =0.98946627 $
$ R-s q=0.97904349 $
s: 0.2141

$\begin{array}{lrr}
\text { Parameter } & \text { coeff } & \text { se } \
\text { Intercept } & 1.1857 & 0.4346 \
\text { height } & 0.1792 & 0.0151
\end{array}$

-Do you think this transformation for linearity was successful? Why?

The Bigger the Stop Sign, the More Expensive It Is R \mathrm{R} R

The Bigger the Stop Sign, the More Expensive It Is $\mathrm{R}$