The ability to take into account several underlying influences (independent variables) on the dependent variable makes multiple linear regression a significant tool for understanding investment issues.
It can be applied to forecasting variables, testing ideas already in place, and determining correlations between variables.
The following is the general multiple linear regression model:
where:
Yi = ith observance of the dependent variable Y, where I = 1, 2,…, n
Independent variables: Xj, where j = 1, 2,…, k
Xji is equal to the ith observation of the jth independent variable.
intercept term b0
For each of the independent variables, bj=slope coefficient
For the ith observation, the error term is i.
number of observations, or n
There are k independent variables.