Comparing Least-Squares Fit and Least Absolute Deviations Fit

The Demonstration compares the least-squares fit method and the least absolute deviations fit method. A straight line is fitted to a set of data points. In the case of the least-squares fit the straight line is obtained by minimizing the sum of the squares of the residuals which are the deviations of the data points from the line. In the case of the least absolute deviations fit the straight li;

Least Squares Criteria for the Least Squares Regression Line - Wolfram Demonstrations Project

Akaike Criterion in Orbit Determination - Wolfram Demonstrations Project

Fitting an Elephant - Wolfram Demonstrations Project

Country Quiz - Wolfram Demonstrations Project

Least-Squares Regression, Line Formula, Method & Examples - Lesson

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate, BMC Bioinformatics

Linear vs Logistic Regression: Detailed Analysis

LADFIT

10.1 - Nonconstant Variance and Weighted Least Squares