32. A power company located in southern Alabama wants to predict the peak power load (i.e., the maximum amount of power that must be generated each day to meet demand) as a function of the daily high temperature (X). A random sample of 25 summer days is chosen, and the peak power load and the high temperature are recorded each day. The file P13_32.xlsx contains these observations. (Note that the data from P13_32.xlsx is already provided in the template). a. Create a scatterplot for these data. Comment on the observed relationship between Y and X. b. Estimate an appropriate regression equation to predict the peak power load for this power |company. Interpret the estimated regression coefficients. c. Analyze the estimated equation’s residuals. Do they suggest that the regression equation is adequate? If not, return to part b and revise your equation. Continue to revise the equation until the results are satisfactory. d. Use your final equation to predict the peak power load on a summer day with a high temperature of 100 degrees.
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32. A power company located in southern Alabama wants to predict the peak power load (i.e., the maximum amount of power that must be generated each day to meet demand) as a
a. Create a scatterplot for these data. Comment on the observed relationship between Y and X.
b. Estimate an appropriate regression equation to predict the peak power load for this power |company. Interpret the estimated regression coefficients.
c. Analyze the estimated equation’s residuals. Do they suggest that the regression equation is adequate? If not, return to part b and revise your equation. Continue to revise the equation until the results are satisfactory.
d. Use your final equation to predict the peak power load on a summer day with a high temperature of 100 degrees.
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