Logistic regression estimation

1. What are estimates in logistic regression?
2. How are logistic regression coefficients estimated?
3. What is estimation in regression?
4. Why maximum likelihood estimation for logistic regression?

What are estimates in logistic regression?

To estimate a logistic regression we need a binary response variable and one or more explanatory variables. We also need specify the level of the response variable we will count as success (i.e., the Choose level: dropdown). In the example data file titanic , success for the variable survived would be the level Yes .

How are logistic regression coefficients estimated?

The coefficient of a continuous predictor is the estimated change in the natural log of the odds for the reference event for each unit increase in the predictor. For example, if the coefficient for time in seconds is 1.4, then the natural log of the odds increase by 1.4 for each additional second.

What is estimation in regression?

A primary use of the estimated regression equation is to predict the value of the dependent variable when values for the independent variables are given. For instance, given a patient with a stress test score of 60, the predicted blood pressure is 42.3 + 0.49(60) = 71.7.

Why maximum likelihood estimation for logistic regression?

The maximum likelihood approach to fitting a logistic regression model both aids in better understanding the form of the logistic regression model and provides a template that can be used for fitting classification models more generally.