Lori Cook has developed the following forecasting model: = 36.0 +4.50%, where g = demand for Kool Air conditioners and x = the outside temperature (degrees Fahrenheit) a) When the temperature outside is 70F, demand forecast - air conditioners (enter your response as an integer) b) When the temperature outside is 80 F demand forecast = | air conditioners (enter your response as an integer). c) When the temperature outside is 90 F, demand forecast - air conditioners (enter your response as an integer). o The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in Portland, to the number of guests registered that week: Week 1 2 Guests 16 12 18 14 Bar Sales $320 $265 $380 $315 a) The simple linear regression equation that relates bar sales to number of guests (not to time) is (round your responses to one decimal place): Bar Sales - Exguests b) If the forecast is 30 guests next wook, the bar sales are expected to be fround your response to ono decimal place). Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons: Week 1 2 3 4 Forecast Method 1 0.90 1.02 0.92 1.17 Actual Demand 0.70 1.05 1.00 1.04 Week 1 2 3 4 Forecast Method 2 0.80 1.19 0.90 1.15 Actual Demand 0.70 1.05 1.00 1.04 The MAD for Method 1 - thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 1 -thousand gallons? (round your response to three decimal places). The MAD for Method - thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 2-thousand gallons? (round your response to three decimal places)