Power-generating facilities used forecasts of temperature to forecast energy demand. The average temperature x (degrees Fahrenheit)...

 

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Power-generating facilities used forecasts of temperature to forecast energy demand. The average temperature x (degrees Fahrenheit) and the day's energy demand y (million watt-hours) were recorded on 40 randomly selected winter days in the region served by a power company. The sample data are summarized by the following information. n = 40 Σx | x = 2000 Σ y = 2969 Σxy = 143,042 40 ≤ x ≤ 60 Σ x2 = 101, 340_ Σ y = 243, 027 Compute the linear regression equation for these sample data and interpret its meaning in the context of the problem. Power-generating facilities used forecasts of temperature to forecast energy demand. The average temperature x (degrees Fahrenheit) and the day's energy demand y (million watt-hours) were recorded on 40 randomly selected winter days in the region served by a power company. The sample data are summarized by the following information. n = 40 Σx | x = 2000 Σ y = 2969 Σxy = 143,042 40 ≤ x ≤ 60 Σ x2 = 101, 340_ Σ y = 243, 027 Compute the linear regression equation for these sample data and interpret its meaning in the context of the problem.

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To compute the linear regression equation also known as the least squares regression line we use the ... View the full answer