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. $The bigger the stop sign, the more expensive it is. Here is a graph of the height of a sign in inches versus its cost in dollars. To achieve linearity, the data was transformed using a square root function of cost. Here are the results and a residual plot.Dependent Variable: sqrt(cost) \mathrm{R} (correlation coefficient) =0.98946627 \mathrm{R}-\mathrm{sq}=0.97904349 s: 0.2141 \begin{array}{lrr} \text { Parameter } & \text { coeff } & \text { se } \\ \text { Intercept } & 1.1857 & 0.4346 \\ \text { height } & 0.1792 & 0.0151 \end{array} -Use your equation to predict the cost of a 48 stop sign.$
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

\mathrm{R}-\mathrm{sq}=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}

$The bigger the stop sign, the more expensive it is. Here is a graph of the height of a sign in inches versus its cost in dollars. To achieve linearity, the data was transformed using a square root function of cost. Here are the results and a residual plot.Dependent Variable: sqrt(cost) \mathrm{R} (correlation coefficient) =0.98946627 \mathrm{R}-\mathrm{sq}=0.97904349 s: 0.2141 \begin{array}{lrr} \text { Parameter } & \text { coeff } & \text { se } \\ \text { Intercept } & 1.1857 & 0.4346 \\ \text { height } & 0.1792 & 0.0151 \end{array} -Use your equation to predict the cost of a 48 stop sign.$
-Use your equation to predict the cost of a 48" stop sign.

Which of These Random Variables Has a Geometric Model

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