Linear regression algorithms fit a straight line, or another function that is linear in its parameters such as a polynomial, to numeric data, typically by performing matrix inversions to minimize ...

Balgobin Nandram, Erik Barry Erhardt, Fitting Bayesian Two-Stage Generalized Linear Models Using Random Samples via the SIR Algorithm, Sankhyā: The Indian Journal of Statistics (2003-2007), Vol. 66, ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

We suggest a one-dimensional jump-detection algorithm based on local polynomial fitting for jumps in regression functions (zero-order jumps) or jumps in derivatives (first-order or higher-order jumps) ...

Linear regression draws corresponding trend lines, such as disease outbreaks, bitcoin prices, demand for software experts, etc.