There are several types of MLR, including:
Type

Characteristics

Direct (or Standard)

 All IVs are entered simultaneously

Hierarchical

 IVs are entered in steps, i.e., some before others
 Interpret: R^{2} change, F change

Forward

 The software enters IVs one by one until there are no more significant IVs to be entered

Backward

 The software removes IVs one by one until there are no more nonsignificant IVs to removed

Stepwise

 A combination of Forward and Backward MLR. Stepwise regression will do the most efficient job of quickly sorting through many IVs and identifying a relatively simple model based only on the statistically significant predictors.

Forward, Backward, and stepwise regression hands the decisionmaking power over to the computer which should be discouraged for theorybased research.
For more information, see Multiple linear regression I (Lecture)