The normal equations in linear regression: The likelihood equations: where is the log-likelihood. The equation which must be solved to do non-linear least squares: The iteratively reweighted least ...

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Linear regression assumes a linear relationship, is sensitive to outliers, and may not perform well if the assumptions (like homoscedasticity or normality) are violated.

Then conditions on the set F and on the xkm are obtained such that the least squares estimators (LSE) of the parameters β1, ⋯, βq are consistent in Case (a) (Theorem 1) or asymptotically normal in ...

We consider the semiparametric linear regression model with censored data and with unknown error distribution. We describe estimation equations of the Buckley-James type that admit $\sqrt ...

You will likely find approach (2) to be the most useful in practice because, in many cases, you will want to change the granularity of your categorical variables. A regression equation with a zillion ...

Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.