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 Videos     Formula 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 Fitting Bishop Textbook – Chapter 1

6. Polynomial Curve Fitting Bishop Textbook – Chapter 1

7. Polynomial Curve Fitting Bishop Textbook – Chapter 1

8. General Regression – not just straight line Least Squares Approximation Introduction 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 versus Principal Component Analysis
Reference
Linear Regression
Principal Component Analysis

17. Linear Regression versus Reverse Linear Regression
Reference
Linear Regression
Reverse Linear Regression