Learn how to estimate linear regression model parameters in machine learning using ordinary least squares and gradient descent. Compare their advantages and disadvantages.

Generalized linear regression models (GLMs) are a powerful tool for analyzing data that do not follow the assumptions of ordinary linear regression, such as non-normality, heteroscedasticity, or ...

In this part, we will fit the linear regression parameters θ to our dataset using gradient descent. computeCost.m : Function to compute the cost of linear regression function J = computeCost(X, y, ...

A method of parameter identification using multiple linear regression is proposed. Under the state of transformer operation, the parameters in the formula are calculated according to the actual ...

Abstract: The paper proposes a method for developing a linear model for Proportional Integral Derivative (PID) parameters of a PID controller. The approach is based on linear regression and it uses ...

Practitioners usually use linear regression modeling to investigate the relationship and effect of some selected explanatory variables on the normal response variable. However, this is not suitable ...