###### Course Contents

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.
Case Study:
Analyze the real data and present the work in the form of report.

###### Course Synopsis

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

View Now

###### Deriving the least squares estimators of the slope and intercept (simple linear regression) by jbstatistics

View Now

###### Econometrics - Simple Linear Regression | Expectation and variance of OLS | Gauss Markov Theorem by Calqulus Classes

View Now

###### Simple Linear Regression: Maximum Likelihood Estimation by statisticsmatt

View Now

###### Simple Linear Regression: Estimating the Residual Variance by statisticsmatt

View Now

###### Gauss markov theorem part 1 by ecopoint

View Now

###### Gauss markov theorem part 2 by ecopoint

View Now

###### SLR Properties of residuals: Σei = 0 , Σei xi = 0 & Σŷi ei = 0 Simple Linear Regression by 118yt118

View Now

###### Proof that total sum of squares = explained sum of squares plus the sum of squared residuals by Easynomics

View Now

###### Introduction to Multiple Linear Regression | What is Regression Techniques | Data Science -ExcelR

View Now

###### Linear Regression and Multiple Regression by CodeEmporium

View Now

###### Least Squares Estimator Vector is Unbiased and Consistent for the Multiple Linear Regression Model by Bill Kinney

View Now

###### The Gauss-Markov Theorem proof - matrix form - part 1 by Ben Lambert

View Now

###### The Gauss-Markov Theorem proof - matrix form - part 2 by Ben Lambert

View Now

###### Stepwise Regression by Pat Obi

View Now

###### Orthogonal Regression by Statgraphics Technologies, Inc.

View Now

###### The Gauss-Markov Theorem proof - matrix form - part 3 by Ben Lambert

View Now

###### Least squares - 13 - Multiple linear regression - Matrix form and an example by Kevin Dunn

View Now

###### Perform Linear Regression Using Matrices by Mathispower4u

View Now

###### Regularization Part 1: Ridge (L2) Regression by StatQuest with Josh Starmer

View Now

###### Regularization Part 2: Lasso(L1) Regression by StatQuest with Josh Starmer

View Now

Book Title : Basic Econometrics

Author : Damondar N.Gujrati

Edition : forth

Publisher : McGraw-Hill

View Now

Book Title : The Theory of Econometrics

Author : A. Koutsoyiannis

Edition : second edition

Publisher :

View Now

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