1. Refer to the data Set 4

Age	AbsentHours (hr)	LengthService (year)
32.03	36.58	6.02
40.32	30.17	5.53
48.82	83.81	4.39
44.60	70.02	3.08
48.44	81.83	2.72
50.75	60.50	10.16
36.22	30.07	4.43
58.43	181.63	6.94
39.85	30.66	13.85
46.55	28.02	4.87
30.79	34.33	4.58
42.80	21.66	19.11
41.86	55.10	2.56
51.01	81.60	5.30
36.91	94.67	11.23
57.90	108.38	3.30
49.52	67.74	6.53
60.60	158.70	4.47
35.87	48.02	3.10
41.94	42.79	4.56
35.50	34.52	5.17
40.03	59.88	12.19
49.76	100.67	4.31
38.11	9.52	3.49
42.87	38.88	19.01
49.94	129.84	5.78
51.56	75.26	4.64
40.41	58.97	6.19
52.45	134.67	4.29
44.61	78.34	4.86
67.09	169.00	2.18
41.25	56.22	2.02
62.05	152.65	5.47
49.91	80.24	5.36
50.08	78.75	1.00
51.61	88.91	3.99
38.68	21.39	20.68
30.30	3.74	7.56
43.69	38.07	0.39
50.05	116.23	5.70
50.01	77.13	3.65
42.63	24.79	4.12
54.05	121.45	4.06
43.91	59.78	4.20
42.59	55.62	5.10
43.26	55.06	5.84
35.62	44.63	3.68
39.26	42.46	5.75
46.95	107.80	2.02
44.11	78.84	12.31
52.79	110.15	4.59
35.63	33.93	3.95
42.43	28.60	3.39
40.97	44.99	3.29
46.77	126.50	1.99
45.30	19.62	5.57
35.49	44.97	8.35
41.66	45.95	4.71
43.51	83.32	1.78
54.00	136.34	2.87
46.22	53.31	4.42
47.66	96.61	4.25
51.99	140.91	2.96
33.37	31.69	2.30
47.60	12.68	4.15
34.66	54.30	13.83
45.48	103.35	6.84
37.74	49.85	3.57
40.49	58.29	1.22
48.63	110.81	5.53
52.19	158.68	5.33
37.91	73.48	4.68
29.41	2.19	4.85
51.17	99.81	4.95
39.46	34.78	1.91

which reports information on Employee's age and absent hours. If the Absent Hours is the dependent variable and the Age is the independent variable.

1. Determine the regression equation and interpret the slope (b) and y-intercept (a) of the equation
2. Estimate the absent hours with age of 45.
3. Calculate and describe the correlation between the age and absent hours.
4. From the data set, can you conclude the linear relationship between the absent hours and length service? How do you support your result?