A power company would like to predict the monthly heating bill for a household in a…

A power company would like to predict the monthly heating bill for a household in a specific county during the month of January. A random sample of households in the county was selected and their January heating bill recorded along with the variables shown below. Use the regresion output shown to the right to complete parts a and b. SF: the square footage of the house Age: the age of the current heating system in years Temp: the thermostat setting, in degrees Fahrenheit, during the day Coefficients Intercept -657.8114 0.0846 Age 2.1612 Temp 10.8352 SF a. Interpret the meaning of all three regression coefficients. Select the correct choice below and, if necessary, fill in the answer boxes within your choice. (Type integers or decimals. Type exact answers.) . Each additional year decreases the monthly heating bill by $ GA. Each additional square foot decreases the monthly heating bill by $ degree decreases the monthly heating bill by $ . . Each additional B. Each additional square foot increases the monthly heating bill by $ 00879. Each additional year increases the monthly heating bill by $ 2.0421. Each additional degree increases the monthly heating bill by $ 10.2007 O C. There is no meaningful interpretation of the regression coefficients for this application. b. Predict the average January heating bill for a household with 2,850 square feet, a heating system that is nine years old, and a thermostat set to 73 degrees during the day. The average heating bill would be $ 270.64. (Round to the nearest cent as needed.)