Binary logistic regression coefficients are estimated through an iterative maximum likelihood method.Answer instructionsTrueFalse

Answer

The statement is True.

Explanation

In binary logistic regression, the relationship between a binary dependent variable and one or more independent variables is modeled. The coefficients in this regression are estimated using the method of maximum likelihood estimation (MLE), which is an iterative process.

The MLE works by determining the values of the coefficients that maximize the likelihood of the observed sample data under the model. Initially, the coefficients are estimated, and based on these estimates, the likelihood function is calculated. The iterative process continues, adjusting the coefficients until convergence is reached, meaning that the updates to the coefficient estimates produce negligible changes in the likelihood function.

This iterative approach is essential because obtaining a closed-form solution for the coefficients is generally not possible in logistic regression. Instead, numerical optimization techniques such as Newton-Raphson or gradient descent are typically employed to find the maximum likelihood estimates.

In summary, the iterative nature of the maximum likelihood method is a fundamental characteristic of estimating coefficients in binary logistic regression.