Econometrics // Lecture 2: "Simple Linear Regression" (SLR) 

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.
Additional Comments:
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": • Econometrics // Lectur...
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