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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": youtu.be/8tAPsX0YuNE

All video, commentary and music is owned by Keynes Academy.

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": youtu.be/8tAPsX0YuNE

All video, commentary and music is owned by Keynes Academy.