Practical Regression: Maximum Likelihood Estimation

This is the eighth in a series of scripts that, when bound in a textbook could be entitled “Practical Regression.” The purpose of the notes is the theoretical content of most statistics texts with practical advice on nearly three decades of experience of the author, with over one hundred years experience of colleagues who have offered leadership combined supplement based. As the title “Practical regression” implies, these notes are a guide to performing regression in practic … Read more »

This is the eighth in a series of scripts that, when bound in a textbook could be entitled “Practical Regression.” The purpose of the notes is the theoretical content of most statistics texts with practical advice on nearly three decades of experience of the author, with over one hundred years experience of colleagues who have offered leadership combined supplement based. As the title “Practical regression” implies, these notes are a guide to performing regression in practice.This technical note discusses maximum likelihood estimation (MLE). The note explains the concept of goodness of fit and why MLE is a powerful alternative to R-squared. The note is a simple example that uses the intuition of the MLE and the calculation of the probability score and the algorithm coefficients has been developed in MLE models.
«Hide

from
David Dranove
Source: Kellogg School of Management
4 pages.
Release Date: 14 May 2012. Prod #: KEL642-PDF-ENG
Practical Regression Maximum Likelihood Estimation HBR case solution