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Logistics Regression is a type of regression analysis used with attribute data. It is used to find the relationship between a probability and a quantity.
A typical application of logistics regression would be:
A call center provides insurance quotations. Callers are put on hold if an operator is not available and some callers hang up before the call is answered. The records of wait times show:
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Wait
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Abandon
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|
Wait
|
Abandon
|
Wait
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Abandon
|
|
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1
|
10
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N
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11
|
28
|
N
|
21
|
60
|
N
|
|
2
|
12
|
N
|
12
|
29
|
N
|
22
|
64
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Y
|
|
3
|
15
|
N
|
13
|
35
|
Y
|
23
|
68
|
Y
|
|
4
|
18
|
N
|
14
|
38
|
Y
|
24
|
75
|
N
|
|
5
|
18
|
N
|
15
|
42
|
N
|
25
|
80
|
N
|
|
6
|
20
|
N
|
16
|
43
|
N
|
26
|
86
|
N
|
|
7
|
22
|
N
|
17
|
44
|
N
|
27
|
89
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Y
|
|
8
|
26
|
N
|
18
|
49
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Y
|
28
|
92
|
N
|
|
9
|
27
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Y
|
19
|
50
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N
|
29
|
97
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Y
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|
10
|
27
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N
|
20
|
52
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Y
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30
|
100
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Y
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(a real dataset would include many more results).
Find a regression equation relating the probability of a caller abandoning the call to the wait time.
Logistics regression uses the Logit Function or the Probit Function.
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