logistic regression

Logistic regression or other methods are now more commonly used.

Here logistic regression is being used as a binary classification model.

Note: independent variables in logistic regression can also be continuous.

It is used as a descriptive statistics, and plays an important role in logistic regression.

With both continuous and categorical data, it would be best to use logistic regression.

Variables were entered into or removed from the logistic regression equation one at a time.

Discriminant function analysis is very similar to logistic regression, and both can be used to answer the same research questions.

A multiple logistic regression model was used to analyze independent predictor variables.

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

However, when discriminant analysis' assumptions are met, it is more powerful than logistic regression.

Logistic regression analyses also indicated a significant linear increase among male and female students in 12th grade.

Logistic regression and other log-linear models are also commonly used in machine learning.

Logistic regression is used to predict the odds of being a case based on the predictor(s).

The latter example specifies using a specific algorithm for logistic regression.

Unlike logistic regression, discriminant analysis can be used with small sample sizes.

Linear regression and logistic regression analysis were used when appropriate.

As such it treats the same set of problems as does logistic regression using similar techniques.

Two multivariable conditional logistic regression models are shown in Table 4.

Logistic regression with binary data is another area in which graphical residual analysis can be difficult.

However, logistic regression cannot be handled this way.

The site predicts game outcomes and rates teams using a logistic regression model based on team efficiency statistics.

Logistic regression can be used for continuous risk factors within genotype strata.

The P value calculated for the interaction term in the logistic regression model was used to determine statistical significance.

Create complex tables and run linear & logistic regressions using Powerstats.

Variables that have independent predictive value in a logistic regression analysis are incorporated into the risk index.