Practical Regression: Maximum Likelihood Estimation Harvard Case Solution & Analysis

This is the eighth in a series of lectures, which, if linked together in the book, could be entitled "Practical Regression." Goal score in addition to the theoretical content of most statistics texts with practical advice based on nearly three decades of experience of the author, along with more than a hundred years experience of colleagues who offered advice. As the title "Practical Regression" suggests, these notes to guide the implementation of regression practice.This technical note discusses the maximum likelihood estimation (MLE). The note explains the concept of the fit and why the MLE is a powerful alternative to the R-squared. The note should be a simple example that develops intuition MLE, as well as estimates of the probability and the calculation of the algorithm used to estimate the coefficients in the MLE models. "Hide
by David Dranove Source: Kellogg School Management 4 pages. Publication Date: May 14, 2012. Prod. #: KEL642-PDF-ENG

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