PROBLEM 1: Analyze the following data using LINEAR TREND ANALYSIS. How many cars will be sold in time period 13? + TIME PERIOD NUMBER OF CARS SOLD 1 70 2 80 3 66 4 74 5 64 6 76 7 72 8 83 9 82 10 76 11 84 12 80 PROBLEM 2: As you can see below, a sample of 20 cars was taken, and the miles per gallon (MPG), horsepower, and total weight were recorded. NOTE: You will get negative independent variables (horsepower & weight) and that is OK. A. Develop a simple regression model to predict the dependent variable, MPG (Y), using horsepower (X1) as the independent variable. Predict the dependent variable, MPG (Y), if horsepower (X1) is forecasted to be 117. B. Develop a simple regression model to predict the dependent variable, MPG (Y), using weight (X2) as the independent variable. Predict the dependent variable, MPG (Y), if weight (X2) is forecasted to be 3426. C. Using R, which of the 2 causal forecasting models, A or B, is better? D. Develop a multiple regression model to predict the dependent variable, MPG (Y), using horsepower (X1) and weight (X2) as the 2 independent variables. E. Using R, how does the multiple regression model compare with each of the 2 simple regression models? F. Using the multiple regression model, predict the dependent variable, MPG (Y), if horsepower (X1) is forecasted to be 117 and weight (X2) is forecasted to be 3426. MPG (Y) HORSEPOWER (X1) WEIGHT (X2) 44 67 1844 44 50 1998 40 62 1752 37 69 1980 37 66 1797 34 63 2199 35 90 2404 32 99 2611 30 63 3236 28 91 2606 26 94 2580 26 88 2507 25 124 2922 22 97 2434 20 114 3248 21 102 2812 18 114 3382 18 142 3197 16 153 4380 16 139 4036