#### Event Title

### An Application of Logistic Regression in Estimation and Prediction

#### Location

CSU

#### Student's Major

Mathematics and Statistics

#### Student's College

Science, Engineering and Technology

#### Mentor's Name

Mezbahur Rahman

#### Mentor's Department

Mathematics and Statistics

#### Mentor's College

Science, Engineering and Technology

#### Description

Logistic regression is used in categorical response variable models. The explanatory variables could be categorical or in a continuous scale. Often, parameters are estimated and goodness-of-fit are studied in logistic models, but the ultimate usefulness of a logistic model depends on the prediction rule of the categorical response. Here we revisit all four aspects of the logistic regression using a data set. The four aspects are parameter estimation, goodness-of-fit of the model, prediction rule, and marginal effect of the factors.

An Application of Logistic Regression in Estimation and Prediction

CSU

Logistic regression is used in categorical response variable models. The explanatory variables could be categorical or in a continuous scale. Often, parameters are estimated and goodness-of-fit are studied in logistic models, but the ultimate usefulness of a logistic model depends on the prediction rule of the categorical response. Here we revisit all four aspects of the logistic regression using a data set. The four aspects are parameter estimation, goodness-of-fit of the model, prediction rule, and marginal effect of the factors.