The following dat were collected by an employment agency to determine if biweekly salaries can be explained in any of the following variables: 1) length of service in months, 2) sex of the employee 3) age of the employee, and 4) whether or not the job is technical or clerical in nature

Salary	Service	Sex	Age	JobType
1700	93		male	42	clerical
1500	104		male	33	clerical
1950	104		male	42	technical
2500	126		male	57	technical
1900	98		male	30	technical
2250	99		male	49	technical
1650	94		male	35	clerical
1550	96		female	46	technical
2100	124		female	56	technical
1650	73		female	23	technical
1900	110		female	67	technical
1985	90		female	36	technical
1455	104		female	53	clerical
1540	81		female	29	clerical
1900	106		male	45	clerical
2000	113		female	55	technical
2186	129		male	46	technical
1600	97		female	39	technical
2200	101		male	43	technical
2100	91		male	35	technical
2030	100		male	40	clerical
2200	123		male	59	clerical
1544	88		female	30	clerical
2350	117		male	60	technical
2350	107		male	45	technical
1900	105		female	32	technical
1400	86		female	33	clerical
2100	131		female	56	technical
1950	95		male	30	technical
1700	98		male	47	clerical

1) Explain all the relevant of your best model e.g. significance of coefficients, F Statistics, co efficiencts of determination (r squared. Then use your final model to make predictions for a male and female with the same job characteristics - for confidence interval and prediction interval