I have some data which I want to run multiple regression on.
1- is multiple regression the right analysis for this problem
2- can someone guide me on how to do this in pandas or Minitab using the data set below
Here is a sample of the data which is for 100 random sales personnel.
The output metric is the amount of revenue per interaction each person has (this can be negative if a customer cancels a sale within 90 days).
The input metrics are the number of sales per unit type out of 100 interactions. Obviously, the more units sold per interaction (3 types of units) the more revenue would be earned per interaction. How can I account for the relationship between these 3 unit type metrics and my output metric? I'd want to be able to say if X1 is 0.75 and X2 is 1.0 and X3 is 0.25 then my Y will be a specific value.
Right now we are driving each metric individually without accounting for their interactions and dependencies which seems inefficient for predicting potential performance.
Person Y X1 X2 X3
1 ($0.81) 0.43 0.54 0.00
2 $3.75 0.67 1.11 0.11
3 $1.76 0.23 0.70 0.00
4 $2.38 0.87 1.24 0.00
5 $5.06 0.62 1.11 0.37
6 $5.35 0.63 1.13 0.25
7 $2.94 0.64 0.76 0.00
8 $2.84 0.51 0.64 0.00
9 $0.35 0.00 0.90 0.00
10 $2.61 0.53 0.92 0.00
11 ($0.31) 0.40 0.27 0.13
12 $4.78 0.41 0.81 0.00
13 $2.76 0.54 1.09 0.00
14 $5.25 0.82 1.09 0.00
15 $2.23 0.14 0.82 0.14
16 $1.45 0.42 0.84 0.00
17 $3.14 0.28 0.99 0.00
18 $4.21 0.71 0.71 0.71
19 $1.33 0.57 0.57 0.00
20 $2.78 0.58 1.01 0.00
21 $1.71 0.29 1.15 0.00
22 $4.43 0.44 0.73 0.15
23 $4.74 0.73 1.17 0.00
24 $1.30 0.44 0.44 0.00
25 $2.68 0.59 0.74 0.15
26 $1.84 0.30 0.74 0.00
27 $3.88 0.74 1.33 0.00
28 $2.11 0.30 0.74 0.00
29 $4.50 0.30 0.60 0.00
30 $3.46 0.60 1.05 0.00
31 $4.07 0.30 1.20 0.00
32 $3.50 0.90 1.20 0.00
33 $1.21 0.30 0.45 0.00
34 $2.55 0.45 0.60 0.15
35 $4.06 0.76 1.06 0.00
36 $0.44 0.46 0.61 0.00
37 $2.00 0.76 0.46 0.00
38 $0.33 0.15 0.77 0.00
39 $2.24 0.61 0.92 0.00
40 $2.81 0.77 1.54 0.00
41 $1.12 0.00 0.31 0.00
42 $1.30 0.15 0.46 0.31
43 $3.05 0.31 1.69 -0.15
44 $3.59 0.62 0.92 0.00
45 $3.17 0.62 1.39 0.00
46 $0.99 0.31 0.00 0.00
47 $2.00 0.63 0.63 0.47
48 $3.90 0.78 1.10 0.00
49 ($0.26) 0.00 0.32 0.00
50 $5.81 0.48 0.95 0.00
51 $1.91 0.16 0.16 0.00
52 $0.55 0.00 0.48 0.00
53 $1.26 0.32 0.64 0.16
54 $2.63 0.80 0.96 0.00
55 $4.00 0.96 1.28 0.00
56 $6.55 0.96 1.59 0.00
57 $1.85 -0.16 0.32 0.32
58 $4.40 1.12 1.60 0.00
59 $0.78 0.32 0.16 0.16
60 $2.33 0.64 0.48 0.16
61 $4.33 0.32 0.97 0.00
62 $2.73 0.97 1.45 0.16
63 $0.89 0.16 0.32 0.00
64 $1.24 0.16 0.32 0.00
65 $2.38 0.33 0.33 0.00
66 $2.97 0.33 0.82 0.00
67 $4.17 0.33 0.82 0.82
68 $1.79 0.33 0.49 0.00
69 $4.14 0.49 0.82 0.00
70 ($0.02) 0.33 0.99 0.00
71 $4.54 0.33 0.83 0.00
72 $3.31 0.50 0.83 0.00
73 $4.71 0.50 1.17 0.00
74 $2.54 0.50 1.01 0.17
75 $2.82 0.34 0.68 0.00
76 $1.76 0.17 0.68 0.00
77 $0.42 0.17 0.34 0.00
78 $2.46 0.51 0.51 0.00
79 $2.75 0.34 0.34 0.00
80 $2.09 0.35 0.69 0.17
81 $3.11 0.52 1.04 0.00
82 $0.79 0.17 0.70 0.00
83 $3.55 0.70 0.87 0.00
84 $0.81 0.52 1.22 0.00
85 $2.50 0.53 0.70 -0.18
86 $4.38 0.35 1.23 0.00
87 $0.59 0.53 0.88 0.00
88 $0.75 0.00 0.35 0.00
89 $2.03 0.18 0.18 0.00
90 $2.33 0.18 0.18 0.00
91 $3.20 0.18 0.36 0.53
92 $0.01 0.00 0.36 0.00
93 $1.97 0.90 0.72 1.08
94 $2.26 0.54 1.44 0.00
95 $4.85 1.09 2.72 0.00
96 $1.05 0.18 0.91 0.00
97 $1.15 0.18 0.18 0.00
98 $3.10 1.09 1.28 0.00
99 $3.11 0.37 1.10 0.00
100 $0.33 -0.18 0.00 0.18
