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Sample of 100 agents Agent ID Years of Experience Type of Experience Performance ($1000) The HR manager of a brokerage company is trying to find
Sample of 100 agents | |||||||||||||||||
Agent ID | Years of Experience | Type of Experience | Performance ($1000) | 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. | |||||||||||||
1 | 1 | Finance and Accounting | 29 | ||||||||||||||
2 | 21 | Sales | 112 | ||||||||||||||
3 | 15 | Operations | 117 | ||||||||||||||
4 | 12 | Finance and Accounting | 82 | ||||||||||||||
5 | 19 | Finance and Accounting | 81 | 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. | |||||||||||||
6 | 6 | Sales | 84 | ||||||||||||||
7 | 23 | Sales | 89 | ||||||||||||||
8 | 6 | Finance and Accounting | 68 | b) Define appropriate dummy variables to model "type of past experience" for regression purposes. | |||||||||||||
9 | 16 | Sales | 125 | ||||||||||||||
10 | 11 | Finance and Accounting | 93 | 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. | |||||||||||||
11 | 15 | Sales | 129 | ||||||||||||||
12 | 30 | Sales | 45 | ||||||||||||||
13 | 10 | Operations | 109 | ||||||||||||||
14 | 22 | Sales | 84 | ||||||||||||||
15 | 3 | Sales | 63 | 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? | |||||||||||||
16 | 20 | Operations | 121 | ||||||||||||||
17 | 30 | Sales | 32 | ||||||||||||||
18 | 8 | Sales | 111 | e) Between these two models, which one would you prefer as your final choice? Why? | |||||||||||||
19 | 13 | Operations | 103 | ||||||||||||||
20 | 26 | Sales | 81 | ||||||||||||||
21 | 22 | Operations | 115 | ||||||||||||||
22 | 20 | Operations | 93 | ||||||||||||||
23 | 27 | Sales | 60 | ||||||||||||||
24 | 28 | Operations | 45 | ||||||||||||||
25 | 15 | Operations | 124 | ||||||||||||||
26 | 27 | Finance and Accounting | 90 | ||||||||||||||
27 | 2 | Operations | 48 | ||||||||||||||
28 | 1 | Finance and Accounting | 23 | ||||||||||||||
29 | 12 | Sales | 122 | ||||||||||||||
30 | 22 | Sales | 131 | ||||||||||||||
31 | 4 | Sales | 69 | ||||||||||||||
32 | 16 | Sales | 123 | ||||||||||||||
33 | 18 | Sales | 133 | ||||||||||||||
34 | 26 | Sales | 64 | ||||||||||||||
35 | 2 | Finance and Accounting | 37 | ||||||||||||||
36 | 12 | Finance and Accounting | 96 | ||||||||||||||
37 | 27 | Sales | 116 | ||||||||||||||
38 | 11 | Operations | 98 | ||||||||||||||
39 | 12 | Finance and Accounting | 79 | ||||||||||||||
40 | 29 | Finance and Accounting | 25 | ||||||||||||||
41 | 16 | Operations | 90 | ||||||||||||||
42 | 17 | Sales | 100 | ||||||||||||||
43 | 27 | Operations | 45 |
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