Page "Logistic regression" Paragraph 4

Logistic regression is used extensively in numerous disciplines, including the medical and social science fields.

For example, the Trauma and Injury Severity Score ( TRISS ), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al using logistic regression.

It is also employed in marketing applications such as prediction of a customer's propensity to purchase a product or cease a subscription, etc.

For example, logistic regression might be used to predict whether a patient has a given disease ( e. g. diabetes ), based on observed characteristics of the patient ( age, gender, body mass index, results of various blood tests, etc .).

Another example might be to predict whether a voter will vote Democratic or Republican, based on age, income, gender, race, state of residence, votes in previous elections, etc.

The technique can also be used in engineering, especially for predicting the probability of failure of a given process, system or product.

In each of these instances, a logistic regression model would compute the relevant odds for each predictor or interaction term, take the natural logarithm of the odds ( compute the logit ), conduct a linear regression analysis on the predicted values of the logit, and then take the exponential function of the logit to compute the odds ratio.

Conditional random fields, an extension of logistic regression to sequential data, are employed in natural language processing.