Basic concepts of Regression
About the Lecture
The lecture Basic concepts of Regression by Edu Pristine is from the course Archiv - Quantitative Analysis. It contains the following chapters:
- Basic concept of regression
- The million dollar question
- Introduction to regression analysis
- Types of regression models
- Population linear regression
- Sample regression function
- The error term (residual)
- OLS regression properties
- The least squares equation
- Assumptions underlining linear regressions
Author of lecture Basic concepts of Regression
Edu Pristine
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Excerpts from the accompanying material
... - Sample Regression Line, - Hypothesis Testing, - Explained and Unexplained Variation, - Residual AnalysisEstimating ...
... with one Regressor Expect around 6–8 questions ...
... analysis is to measure how the changes in one variable known as dependent variable can be explained by the changes in one or more other variables called the independent variables Linear relationship ...
... Dependent variable: the variable we wish to explain ...
... Random Error term, or residual Dependent Variable ...
... Estimated (or predicted) y value. Estimate of the regression slope. ...
... Wouldn’t it be good if we were able to reduce this error term? ...
... Are there any advantages of minimizing ...
... The simple regression line always passes through the mean of the y variable and the mean 0 ...
... More often than not it does not have a physical interpretation ...
... Error values u for given Xiare statistically independent, their covariance is zero. Once we fulfill these assumptions in Linear Regression, we are able to estimate the variance. ...
