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
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Econometrics - Simple Linear Regression | Expectation and variance of OLS | Gauss Markov Theorem by Calqulus Classes
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Simple Linear Regression: Maximum Likelihood Estimation by statisticsmatt
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Simple Linear Regression: Estimating the Residual Variance by statisticsmatt
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Gauss markov theorem part 1 by ecopoint
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Gauss markov theorem part 2 by ecopoint
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SLR Properties of residuals: Σei = 0 , Σei xi = 0 & Σŷi ei = 0 Simple Linear Regression by 118yt118
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Proof that total sum of squares = explained sum of squares plus the sum of squared residuals by Easynomics
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Introduction to Multiple Linear Regression | What is Regression Techniques | Data Science -ExcelR
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Linear Regression and Multiple Regression by CodeEmporium
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Least Squares Estimator Vector is Unbiased and Consistent for the Multiple Linear Regression Model by Bill Kinney
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The Gauss-Markov Theorem proof - matrix form - part 1 by Ben Lambert
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The Gauss-Markov Theorem proof - matrix form - part 2 by Ben Lambert
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Stepwise Regression by Pat Obi
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Orthogonal Regression by Statgraphics Technologies, Inc.
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The Gauss-Markov Theorem proof - matrix form - part 3 by Ben Lambert
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Least squares - 13 - Multiple linear regression - Matrix form and an example by Kevin Dunn
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Perform Linear Regression Using Matrices by Mathispower4u
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Regularization Part 1: Ridge (L2) Regression by StatQuest with Josh Starmer
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Regularization Part 2: Lasso(L1) Regression by StatQuest with Josh Starmer
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Book Title : Basic Econometrics
Author : Damondar N.Gujrati
Edition : forth
Publisher : McGraw-Hill
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Book Title : The Theory of Econometrics
Author : A. Koutsoyiannis
Edition : second edition
Publisher :
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Title : simple linear regression
Type : Presentation
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Title : Properties of least square estimators
Type : Presentation
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Title : Basic Econometrics
Type : Reference Book
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Title : General Linear Regression model in partitioned form
Type : Other
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Title : ANOVA in general linear regression model and hypothesis testing
Type : Other
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