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Function documentationMLR Standard Forecast

 

Multiple linear regression (MLR) is a statistical technique that can be used to analyze the relationship between a single dependent variable and several independent variables. For the MLR standard method, SAP APO uses the standard method of the least squares (SCM-APO-FCS).

Features

The objective of multiple regression analysis is to use the independent (or explanatory) variables whose values are known in the past and can be projected into the future to predict the future values of the single dependent variable. Each predictor variable (Xi) is weighted, and the weights (ßi) denote their relative contribution to the overall prediction. In calculating the weights (model parameters), the regression analysis procedure ensures a best possible prediction from the set of independent variables. These weights also facilitate interpretation as to the influence of each variable making the prediction, although correlation among the independent variables can complicate the interpretative process.

The general notation for MLR is:

Y = b0 + b1X1 + b2X2 + b3X3...ßnXn + ei

where:

Y = independent variable

ß0 = Y-intercept or constant

ßi = coefficient or weights

Xi = independent variables

ei = remaining error or forecast error

The assumptions of an MLR model are as follows:

  • The Xs are non-stochastic.

  • No exact linear relationship exists between two or more of the explanatory variables.

  • Errors corresponding to different observations are independent and therefore uncorrelated.

  • The error variable is distributed normally or has a Poisson distribution.

  • The error variable has an expected value of 0.

Activities

You make the settings for the MLR standard forecast run in the MLR profile. For example, here you decide which distribution is to be used and whether the variance is constant for all observations or whether it is variable. For more information, see MLR profile.