1. Multiple Regression
A curvilinear relationship between one variable and the values of two or more other independent variables.
Y = intercept + (slope1 times X1) + (slope2 times X2) + etc.

2. Multiple Regression Assumptions
A linear relationship exists between the independent and dependent variables
All variables are normally distributed
No or little multicollinearity (when independent variables are not independent from each other)
No auto-correlation (when residuals are not independent from each other)
Homoscedasticity - The variance of errors is the same across all levels of the independent variable

3. Picking the right independent variables

4. Problem with multiple regression
If you add enough variables (around 10 or more) you can make show a good fitting relationship between Y and pretty much anything.