The process of using past cost information to predict future costs is called cost estimation. While many methods are used for cost estimation, the least-squares regression method of cost estimation is ...

The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate correlation coefficient. If you look at the multiple regression we did, ...

For the model x and y are considered variables. But in the process of fitting the experimental points x i and y i are fixed whereas a and b are varied until the best match between the experimental ...

We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) ...

A statistical technique for fitting a curve to a set of data points. One of the variables is transformed by taking its logarithm, and then a straight line is fitted to the transformed set of data ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...