Utility companies, which must plan the operation and expansion of electricity

Chapter 11, Problem 11.92

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Utility companies, which must plan the operation and expansion of electricity generation, are vitally interested in predicting customer demand over both short and long periods of time. A short-term study was conducted to investigate the effect of each months mean daily temperature x1 and of cost per kilowatt-hour, x2 on the mean daily consumption (in kWh) per household. The company officials expected the demand for electricity to rise in cold weather (due to heating), fall when the weather was moderate, and rise again when the temperature rose and there was a need for air conditioning. They expected demand to decrease as the cost per kilowatt-hour increased, reflecting greater attention to conservation. Data were available for 2 years, a period during which the cost per kilowatt-hour x2 increased due to the increasing costs of fuel. The company officials fitted the model Y = 0 + 1x1 + 2x 2 1 + 3x2 + 4x1x2 + 5x 2 1 x2 + to the data in the following table and obtained y = 325.60611.383x1 +.113x 2 1 21.699x2 + .873x1x2 .009x 2 1 x2 with SSE = 152.177. Mean Daily Consumption Price per kWh ( x2) (kWh) per Household 8 Mean daily F temperature (x1) 31 34 39 42 47 56 Mean daily consumption (y) 55 49 46 47 40 43 10 Mean daily F temperature (x1) 32 36 39 42 48 56 Mean daily consumption (y) 50 44 42 42 38 40 8 Mean daily F temperature (x1) 62 66 68 71 75 78 Mean daily consumption (y) 41 46 44 51 62 73 10 Mean daily F temperature (x1) 62 66 68 72 75 79 Mean daily consumption (y) 39 44 40 44 50 55 When the model Y = 0 1x1 +2x 2 1 + was fit, the prediction equation was y = 130.009 3.302x1 + .033x 2 1 with SSE = 465.134. Test whether the terms involving x2(x2, x1x2, x 2 1 x2) contribute to a significantly better fit of the model to the data. Give bounds for the attained significance level.