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.

1. Develop a simple regression model to predict the dependent variable, MPG, using horsepower as the independent variable.
2. Develop a simple regression model to predict the dependent variable, MPG, using weight as the independent variable.
3. Using R, which of the 2 causal forecasting models, A or B, is better?
4. Develop a multiple regression model to predict the dependent variable, MPG, using horsepower and weight as the 2 independent variables.
5. Using R, how does the multiple regression model compare with each of the 2 simple regression models?
6. 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