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# Introduction to Linear Regression

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1. Introduction to Linear Regression Conceptual Data Analysis Series

2. Episode Objectives What is linear regression? When would I use linear regression? How is a regression line calculated?

3. Correlation

4. Correlation

5. Correlation

6. Regression

7. Regression

8. Application

9. Application

10. Application

11. Application

12. Application

13. Regression Lines Y = mX + b Y’ = bX+ a

14. Regression Lines Y = mX + b Y’ =

15. Regression Lines Y = mX + b Y’ = 2X + 0

16. Regression Lines Y = mX + b Y’ = 2X + 0 Y’ = 2(5) + 0 = 10

17. Regression Lines Y = mX + b Y’ = 2X + 0 Y’ = 2(5) + 0 = 10 Y’ = 2(6.2) + 0 = 12.4

18. Regression Lines Y = mX + b Y’ = 1.9791x + 0.1773

19. Residuals

20. Residuals residual

21. Calculating the Equation

22. Review Regression is an extension of correlation Regression permits us to can predict values of Y based on X, and vice versa Causal statements still requires good experimental research design