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Introduction to Linear Regression

Introduction to Linear Regression

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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