Maximum Likelihood Estimation of Regression — Normal Equation Explained

This Video details how to Maximize the parameters of of Regression Normal Equation using Maximum Likelihood Estimation (MLE). This concise tutorial derives the likelihood for the Regression Normal Equation and shows how maximizing it yields the familiar closed-form normal equation (X^T X)^{-1} X^T y, and explains assumptions, interpretation, and when the closed form applies. Don't forget to Subscribe, like and share for more Statistical tutorials. Need Assistance/Tutor: Send a Message on WhatsApp +2348035415248 +2348056105017 #linearregression #MLE #MaximumLikelihood #NormalEquation #OLS #Statistics #MachineLearning #DataScience #GaussianNoise #LinearAlgebra #RegressionAnalysis #ModelEstimation #SupervisedLearning #MathTutorial #StatisticalInference #Analytics #BiasVariance #Multicollinearity #GradientDescent #ClosedFormSolution