Doque Power Company wants to develop a regression model to help predict its daily peak power demand. This prediction is useful in determining how much generating capacity needs to be available (or purchased from competitors) on a daily basis. The daily peak power demand is influenced primarily by the weather and the day of the week. a. Build a simple linear regression model to predict peak power demand using maximum daily temperature. What is the estimated regression equation?
b. Prepare a line chart plotting the actual peak demand data against the values predicted by this regression equation. How well does the model fit the data?
c. Interpret the R2 statistic for this model.
d. Build a multiple linear regression model to predict peak power demand using maximum daily temperature and the day of the week as independent variables. (Note: This model will have seven independent variables.) What is the estimated regression equation?
e. Prepare a line chart plotting the actual peak demand data against the values predicted by this regression equation. How well does the model fit the data?
f. Interpret the R2 statistic for this model.
g. Using the model you developed in part d, what is the estimated peak power demand Duque should expect on a Wednesday in July when the daily high temperature is forecasted to be 94 degrees?
h. Compute a 95% prediction interval for the estimate in the previous question. Explain the managerial implications of this interval for Duque.