| 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:
| |
Wait |
Abandon |
|
Wait |
Abandon |
Wait |
Abandon |
|
| 1 |
10 |
N |
11 |
28 |
N |
21 |
60 |
N |
| 2 |
12 |
N |
12 |
29 |
N |
22 |
64 |
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 |
Y |
| 8 |
26 |
N |
18 |
49 |
Y |
28 |
92 |
N |
| 9 |
27 |
Y |
19 |
50 |
N |
29 |
97 |
Y |
| 10 |
27 |
N |
20 |
52 |
Y |
30 |
100 |
Y |
(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.
|
