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. 1. Describe why performing linear regression with these data is not a good decision. 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) R (correlation coefficient) = 0.98946627 R-sq = 0.97904349 s: 0.2141 2. Do you think this transformation for linearity was successful? Why? 3. Write the transformed regression equation. Make sure to define any variables used in your equation. 4. Interpret R-sq in the context of this problem. 5. Use your equation to predict the cost of a 48" stop sign. Parameter coeff se Intercept 1.1857 0.4346 height 0.1792 0.0151