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.

Simple Linear Regression

Simple Linear Regression Khan Academy Videos     Formula Derivation (4 parts)     Examples (2 parts)     R-squared or coefficient of determination (2 parts)

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)}

General Regression – not just straight line Polynomial Curve Fitting Bishop Textbook – Chapter 1

Polynomial Curve Fitting Bishop Textbook – Chapter 1

Polynomial Curve Fitting Bishop Textbook – Chapter 1

General Regression – not just straight line Least Squares Approximation Introduction to Algorithms, Cormen, et al., MIT Press

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)}

Linear Regression versus Principal Component Analysis Reference Linear Regression Principal Component Analysis

Linear Regression versus Reverse Linear Regression Reference Linear Regression Reverse Linear Regression