Data Analysis Exercise #1

In 1998, the human resources management (HRM) section at Great Northern Insurance (GNI) decided to investigate the salaries of its claims adjusters. The HRM people were concerned about possible salary inequities among the various groups. In addition, they are hoping to determine a reasonable formula upon which to base salaries for claims adjusters to be hired in the future. To support their investigation, the company randomly chose a number of adjusters and then collected several items of information for each of them. A subset of these data is indicated below. (The entire data set will be made available at a later date.) The data include observations on: Test Score, the result of an IQ-type test; Gender (0 if male, 1 if female); Years School, the number of years of formal schooling; Years Experience, the number of years of experience in claims adjusting; Performance Rating, the most recent job performance rating, based upon scaled judgemental ratings by supervisors; Race (1 if White, 2 if Black, 3 if Asian, and 4 if Other); and Salary, in thousands of dollars per year. Put yourself into the role of the HRM people and think about what information might be helpful. To get started with this process, you should begin by analyzing the data that have been collected.

Test Score	*Gender*	Years School	Years Experience	Performance Rating	*Race*	*Salary*
*(M)*	*(T)*	*(U)*	*(V)*	*(W)*	*(X)*	*(Y)*
91	0	13	0	271	1	37.6
102	0	14	5	400	1	40.7
100	0	12	1	101	1	24.2
117	0	12	1	150	4	29.0
122	1	14	2	337	1	35.5
115	1	12	0	443	4	39.7
97	1	15	1	150	1	29.0
109	0	12	3	145	2	24.1
108	1	12	1	221	2	32.0
104	1	15	0	133	2	24.5
108	1	12	6	269	2	37.5
118	1	14	0	211	3	26.6
103	0	17	0	72	2	23.0
123	1	16	2	405	3	42.8
123	1	14	1	283	1	30.9
103	0	12	0	456	1	38.8
106	0	15	4	422	2	36.2
102	1	16	2	209	3	28.9
118	1	12	3	367	1	39.0
100	1	12	4	354	1	37.1