The HR manager of a brokerage company is trying to find a model to predict their employees' performances. They are planning to use Experience as an important potential predictor for the Annual Sale of their agents. This sheet includes information about "years of experience" and "type of the past occupation" for a sample of 100 agents working at the brokerage company.
a) Draw a scatter chart showing the association between performance (Y) and years of experience (X) with different colors for different experience type levels. Please note you need to prepare ONE scatter chart with THREE separate data series.
b) Define appropriate dummy variables to model "type of past experience" for regression purposes.
c) Run a regression model to predict performance based on the two factors that measure experience (i.e., years of experience and type of experience). What is the equation? How much of the sample variation of agents' performance does this model explain? Is years of experience a significant factor? How about the type of past experience? You first need to check the necessary conditions and comment on multicollinearity effect.
d) Run another regression model predicting performance based on type of past occupation, years of experience, and squared years of experience. What is the equation? Are necessary conditions of residuals satisfied?
Sample of 100 agents
Agent ID	Years of Experience	Type of Experience	Performance ($1000)
1	28	Sales	111
2	19	Finance and Accounting	94
3	19	Operations	88
4	6	Sales	98
5	29	Sales	41
6	29	Finance and Accounting	56
7	30	Operations	53
8	17	Sales	111
9	24	Finance and Accounting	76
10	22	Operations	77
11	20	Operations	131
12	14	Sales	132
13	28	Operations	97
14	10	Finance and Accounting	75
15	12	Finance and Accounting	102
16	8	Operations	93
17	1	Sales	45
18	14	Finance and Accounting	115
19	8	Operations	86
20	22	Finance and Accounting	90
21	24	Operations	67
22	20	Sales	100
23	26	Finance and Accounting	98
24	20	Operations	88
25	2	Finance and Accounting	33
26	11	Sales	94
27	11	Finance and Accounting	98
28	27	Operations	103
29	11	Finance and Accounting	89
30	22	Sales	85
31	14	Sales	95
32	11	Operations	97
33	22	Operations	84
34	24	Operations	117
35	20	Operations	86
36	6	Sales	98
37	6	Operations	67
38	11	Operations	109
39	28	Finance and Accounting	92
40	26	Finance and Accounting	45
41	29	Operations	31
42	11	Sales	99
43	8	Sales	88
44	25	Sales	74
45	1	Finance and Accounting	28
46	15	Finance and Accounting	112
47	13	Sales	103
48	4	Operations	57
49	14	Sales	106
50	14	Operations	119
51	17	Sales	99
52	25	Operations	56
53	5	Operations	62
54	24	Sales	123
55	25	Finance and Accounting	55
56	2	Sales	46
57	11	Sales	105
58	22	Finance and Accounting	84
59	9	Sales	112
60	11	Operations	104
61	15	Finance and Accounting	95
62	14	Operations	91
63	25	Sales	104
64	16	Finance and Accounting	114
65	6	Finance and Accounting	55
66	16	Finance and Accounting	83
67	8	Finance and Accounting	79
68	3	Sales	60
69	15	Operations	84
70	28	Operations	42
71	19	Finance and Accounting	92
72	29	Finance and Accounting	50
73	18	Operations	85
74	4	Finance and Accounting	48
75	26	Finance and Accounting	68
76	29	Operations	87
77	7	Finance and Accounting	63
78	26	Finance and Accounting	102
79	22	Finance and Accounting	88
80	11	Finance and Accounting	94
81	15	Operations	103
82	8	Sales	111
83	14	Sales	95
84	9	Sales	97
85	12	Sales	99
86	1	Sales	36
87	5	Sales	69
88	29	Operations	33
89	10	Sales	103
90	20	Sales	104
91	21	Finance and Accounting	90
92	14	Finance and Accounting	82
93	19	Sales	117
94	28	Finance and Accounting	59
95	14	Finance and Accounting	113
96	6	Sales	89
97	28	Operations	100
98	26	Operations	79
99	21	Sales	110
100	16	Operations	92