How To Fix /SAPAPO/MLR_IN_UNIV002 - Error in MLR calculation; effect of causal variables not considered

/SAPAPO/MLR_IN_UNIV002 - Overview

• Message type: E = Error

• Message class: /SAPAPO/MLR_IN_UNIV -

• Message number: 002

• Message text: Error in MLR calculation; effect of causal variables not considered

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/MLR_IN_UNIV002 - Error in MLR calculation; effect of causal variables not considered ?

  The SAP error message /SAPAPO/MLR_IN_UNIV002 indicates that there is an issue with the Multiple Linear Regression (MLR) calculation in the SAP Advanced Planning and Optimization (APO) module. Specifically, this error suggests that the effect of causal variables has not been considered in the MLR calculation, which can lead to inaccurate forecasting or planning results.
  
  Cause:
  
  Missing Causal Variables: The error typically arises when the MLR model is set up without including the necessary causal variables that are supposed to influence the dependent variable.
  Incorrect Model Configuration: The model may not be configured correctly, leading to the exclusion of important variables.
  Data Issues: There may be issues with the data itself, such as missing values or incorrect data types that prevent the model from recognizing the causal variables.
  Model Type: The model type selected may not be appropriate for the data being analyzed.
  
  
  Solution:
  
  
  Review Model Configuration:
  
  Check the configuration of the MLR model to ensure that all relevant