Regression Analysis

1. Regression Analysis • Jared Dean as quoted in Big Data, Data Mining, and Machine Learning • From my experience, regression is the most dominant force in driving business decisions today. Regression analysis has many useful characteristics; one is the easy interpretation of results. Regression concepts are widely understood, and the methodology is well developed such that a well-tuned regression model by a skilled practitioner can outperform many algorithms that are gaining popularity from the machine learning discipline.

2. Simple Linear Regression

3. Simple Linear Regression Khan Academy VideosFormula Derivation (4 parts)Examples (2 parts)R-squared or coefficient of determination (2 parts)

4. Simple Linear Regression Linear regression calculator Compute the equation for the least-squares, best-fit line through the 5 points {(2,2), (0,0), (-2,-2), (-1,1), (1,-1)}

5. General Regression – not just straight line Polynomial Curve FittingBishop Textbook – Chapter 1

6. Polynomial Curve FittingBishop Textbook – Chapter 1

7. Polynomial Curve FittingBishop Textbook – Chapter 1

8. General Regression – not just straight lineLeast Squares ApproximationIntroduction to Algorithms, Cormen, et al., MIT Press

15. Least Squares Approximation • Pseudoinverse of Matrix A - Linear Example • Using the pseudoinverse method Compute the equation for the least-squares, best-fit line through the 5 points {(2,2), (0,0), (-2,-2), (-1,1), (1,-1)}

16. Linear Regression versusPrincipal Component Analysis Reference Linear Regression Principal Component Analysis

17. Linear Regression versusReverse Linear Regression Reference Linear Regression Reverse Linear Regression