Difference between revisions of "Logistic Regression"

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[[Category:Data Analysis Functions]]
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Logistic regression is a techique for predicting a Bernoulli (i.e., 0,1-valued) random variable from a set of continuous dependent variables.  See the [http://en.wikipedia.org/wiki/Logistic_regression Wikipedia article on Logistic regression] for a simple description.
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= Logistic_Regression( Y,B,I,K ) =
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(''Requires Analytica Optimizer'')
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The Logistic_regression function returns the best-fit coefficients, c, for a model of the form
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<math>
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logit(p_i) = ln\left( {{p_i}\over{1-p_i}} \right) = \sum_k c_k B_{i,k}
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</math>
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given a data set basis B, with each sample classified as y_i, having a classification of 0 or 1.
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The syntax is the same as for the Regression function.  The basis may be of a generalized linear form, that is, each term in the basis may be an arbitrary non-linear function of your data; however, the logit of the prediction is a linear combination of these.
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Once you have used the Logistic_Regression function to compute the coefficients for your model, the predictive model that results returns the probability that a given data point is classified as 1. 
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= Library =
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Generalized Regression.ana
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= See Also =
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* [[Probit_Regression]]
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* [[Regression]]

Revision as of 05:14, 11 May 2007


Logistic regression is a techique for predicting a Bernoulli (i.e., 0,1-valued) random variable from a set of continuous dependent variables. See the Wikipedia article on Logistic regression for a simple description.

Logistic_Regression( Y,B,I,K )

(Requires Analytica Optimizer)

The Logistic_regression function returns the best-fit coefficients, c, for a model of the form [math]\displaystyle{ logit(p_i) = ln\left( {{p_i}\over{1-p_i}} \right) = \sum_k c_k B_{i,k} }[/math] given a data set basis B, with each sample classified as y_i, having a classification of 0 or 1.

The syntax is the same as for the Regression function. The basis may be of a generalized linear form, that is, each term in the basis may be an arbitrary non-linear function of your data; however, the logit of the prediction is a linear combination of these.

Once you have used the Logistic_Regression function to compute the coefficients for your model, the predictive model that results returns the probability that a given data point is classified as 1.

Library

Generalized Regression.ana

See Also

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