Simple linear regression model, its assumptions, least square estimators, Maximum likelihood estimators, test of hypothesis, significance test and confidence intervals.
General linear regression model, its assumptions, least square estimators, Maximum likelihood estimators, tests of hypothesis, tests of significance of a single and complete regression and confidence intervals.
Tests of linear combinations of regression coefficients, use of extraneous information in linear regression, Stepwise regression, Polynomial regression, Orthogonal Polynomial, Orthogonal regression analysis.
Analyze the real data and present the work in the form of report.
To gain the concept in case of cross-sectional data and to describe the statistical relationship between variables in the form of model and ability to build different models in certain given conditions.
Course Learning Outcomes
The learning outcomes associated with course are aimed at students being able to
• Develop a deeper understanding of the linear regression model and general linear model and their limitations
• Know how to diagnose and apply correct model to some problems with the general linear model.
• Develop a greater familiarity of techniques and methods used for statistical inference of regression analysis.
An Introduction to Regression Analysis, Analytics University
Deriving the least squares estimators of the slope and intercept (simple linear regression) by jbstatistics
Econometrics - Simple Linear Regression | Expectation and variance of OLS | Gauss Markov Theorem by Calqulus Classes
Simple Linear Regression: Maximum Likelihood Estimation by statisticsmatt
Simple Linear Regression: Estimating the Residual Variance by statisticsmatt
Gauss markov theorem part 1 by ecopoint
Gauss markov theorem part 2 by ecopoint
SLR Properties of residuals: Σei = 0 , Σei xi = 0 & Σŷi ei = 0 Simple Linear Regression by 118yt118
Proof that total sum of squares = explained sum of squares plus the sum of squared residuals by Easynomics
Introduction to Multiple Linear Regression | What is Regression Techniques | Data Science -ExcelR
Linear Regression and Multiple Regression by CodeEmporium
Least Squares Estimator Vector is Unbiased and Consistent for the Multiple Linear Regression Model by Bill Kinney
The Gauss-Markov Theorem proof - matrix form - part 1 by Ben Lambert
The Gauss-Markov Theorem proof - matrix form - part 2 by Ben Lambert
Stepwise Regression by Pat Obi
Orthogonal Regression by Statgraphics Technologies, Inc.
The Gauss-Markov Theorem proof - matrix form - part 3 by Ben Lambert
Least squares - 13 - Multiple linear regression - Matrix form and an example by Kevin Dunn
Perform Linear Regression Using Matrices by Mathispower4u
Regularization Part 1: Ridge (L2) Regression by StatQuest with Josh Starmer
Regularization Part 2: Lasso(L1) Regression by StatQuest with Josh Starmer
Book Title : Basic Econometrics
Author : Damondar N.Gujrati
Edition : forth
Publisher : McGraw-Hill
Book Title : The Theory of Econometrics
Author : A. Koutsoyiannis
Edition : second edition
Title : simple linear regression
Type : Presentation
View simple linear regression
Title : Properties of least square estimators
Type : Presentation
View Properties of least square estimators
Title : Basic Econometrics
Type : Reference Book
View Basic Econometrics
Title : General Linear Regression model in partitioned form
Type : Other
View General Linear Regression model in partitioned form
Title : ANOVA in general linear regression model and hypothesis testing
Type : Other
View ANOVA in general linear regression model and hypothesis testing