forecasting for mortality using a logistic regression

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I need to know how to do a forecasting for mortality using a logistic regression, can somebody help?

data = {{1., 0.75}, {2., 0.89}, {3., 0.42}, {4., 0.99}, {5., 0.84}, {6., 0.34}, {7., 0.83}, {8., 0.93}, {9., 0.76}, {10., 0.11}}; nlm = NonlinearModelFit[data, b Exp[m x]/(1 + b Exp[m x]), {b, m}, x]

the data that has been input is fake, i want to know if am doing a forecasting what kind of data should i input?

edited Jun 3, 2016 at 5:47

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asked Jun 3, 2016 at 5:13

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$\begingroup$ See How can I consistently get a good logistic regression fit? may be a dup $\endgroup$

user9660
– user9660

2016-06-03 05:29:51 +00:00
Commented Jun 3, 2016 at 5:29
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$\begingroup$ Pleae note that there is LogitModelFit and in this case the more general GeneralizedLinearModelFit to do this. A Logistic Regression is a linear model with the Logit-function as its link function. $\endgroup$

gwr
– gwr

2016-06-03 06:25:45 +00:00
Commented Jun 3, 2016 at 6:25

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So while I can't see your data to make sense, in principle this should work:

data = {{1., 0.75}, {2., 0.89}, {3., 0.42}, {4., 0.99}, {5.,0.84}, {6., 0.34}, {7., 0.83}, {8., 0.93}, {9., 0.76}, {10.,0.11}}; lm = LogitModelFit[ data, x, x ]; lm[11]

0.50088

answered Jun 3, 2016 at 13:55

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