Dec 4, 2014 · When we say, to regress Y Y against X X, do we mean that X X is the independent variable and Y the dependent variable? i.e. Y = aX + b Y = a X + b.

I'm trying to fit a multiple linear regression model to my data with couple of input parameters, say 3.

Oct 19, 2011 · LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value …

With linear regression with no constraints, R2 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r. A negative R2 R 2 is only possible with linear …

Feb 4, 2025 · Belsley (1980) mentioned how DFBETA are calculated for linear regression models "DFBETA values are usually calculated via equations that relate the least-squares fit of a …

Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the for all points combined can't be "correct" if …

Jun 5, 2012 · In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean …

What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?

Nov 14, 2010 · Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that …

Hence, if the sum of squared errors is to be minimized, the constant must be chosen such that the mean of the errors is zero.) In a simple regression model, the constant represents the Y …