OMGT3223 LR Example

OMGT 3223 Linear Regression Example

Table 1.1

t Year Y=Tech Employment (000)
X1=Number of New Tech Degree Holders
X2=Consumer Confidence Index X3=Exports ($000,000)

1 1970 76 4104 90 932

2 1971 82 5546 80 965

3 1972 107 6613 103 1341

4 1973 120 7230 98 1717

5 1974 135 7545 71 2198

6 1975 143 8499 75 2229

7 1976 159 9421 94 2588

8 1977 187 10435 98 3264

9 1978 224 12010 106 4194

10 1979 271 15041 92 5500

11 1980 304 19591 74 7606

12 1981 337 25453 77 8493

13 1982 365 30093 59 8957

14 1983 416 38613 86 10300

15 1984 474 46227 102 13511

16 1985 542 50303 100 13964

17 1986 588 48444 95 13266

18 1987 629 44148 103 13929

19 1988 673 40419 115 18137

20 1989 736 37561 117 22345

21 1990 772 35083 92 24111

22 1991 797 34859 69 25872

23 1992 836 35168 62 24879

24 1993 893 35426 66 25276

25 1994 959 35614 91 28956

26 1995 1090 35802 100 34294

27 1996 1208 35990 105 37621

28 1997 1411 36178 125 41380

29 1998 1599 36366 138 44100

Using the data provided in Table 1.1 to create a multiple linear regression model to forecast tech employment. Define your independent variables and your dependent variable. Construct a general model from the dependent and independent variables. Discuss and interpret the relevant statistics associated with your model, show the final linear regression equation, and illustrate its use.

a. What is the linear regression equation for sales?

b. Calculate the MAD for the linear regression forecasting method.

MAD =

t	Year	Y=Tech Employment (000)	X1=Number of New Tech Degree Holders	X2=Consumer Confidence Index	X3=Exports ($000,000)
1	1970	76	4104	90	932
2	1971	82	5546	80	965
3	1972	107	6613	103	1341
4	1973	120	7230	98	1717
5	1974	135	7545	71	2198
6	1975	143	8499	75	2229
7	1976	159	9421	94	2588
8	1977	187	10435	98	3264
9	1978	224	12010	106	4194
10	1979	271	15041	92	5500
11	1980	304	19591	74	7606
12	1981	337	25453	77	8493
13	1982	365	30093	59	8957
14	1983	416	38613	86	10300
15	1984	474	46227	102	13511
16	1985	542	50303	100	13964
17	1986	588	48444	95	13266
18	1987	629	44148	103	13929
19	1988	673	40419	115	18137
20	1989	736	37561	117	22345
21	1990	772	35083	92	24111
22	1991	797	34859	69	25872
23	1992	836	35168	62	24879
24	1993	893	35426	66	25276
25	1994	959	35614	91	28956
26	1995	1090	35802	100	34294
27	1996	1208	35990	105	37621
28	1997	1411	36178	125	41380
29	1998	1599	36366	138	44100