An Introduction to the "Simple Linear Regression" (SLR) in Econometrics. This video covers:
1. A formal introduction to the SLR model
2. The difference between population and estimation models
3. A basic interpretation of the slope and intercept
4. What causality means
5. A more formal visual representation of the simple linear regression
6. Introduction to residuals
7. An outline of how to estimate the slope and intercept and where it originates from
Note: All of this applies to the "Ordinary Least Squares" (OLS) Estimation.
This video is to serve as a basic introduction to the "Simple Linear Regression" model. The video briefly touches on lots of subjects to ensure that the student gains a strong foundation for more in depth analysis to come.
If you want to estimate any ui, find the estimates for the intercept and slope and plug them into the ui equation: ui = yi - yi_hat = yi - (beta0_hat) - (beta1_hat)(xi). Additionally, remember that the derivative of y in respect to x represents the change in y as a result of a change in x. Therefore if we have a causal relationship, if x increases by 1, y will increase by Beta_1. This will be shown in depth in a later video.
The next video tutorial on "Ordinary Least Squares" and "Goodness Of Fit": youtu.be/8tAPsX0YuNE
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