How To Fix THM_CENTRAL704 - Valid range of R^2 is between 0 and 1

SAP Error Message - Details

• Message type: E = Error

• Message class: THM_CENTRAL - Central messages for E-HA

• Message number: 704

• Message text: Valid range of R^2 is between 0 and 1

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 THM_CENTRAL704 - Valid range of R^2 is between 0 and 1 ?

  The SAP error message "THM_CENTRAL704 Valid range of R^2 is between 0 and 1" typically occurs in the context of statistical analysis or modeling within SAP systems, particularly when dealing with regression analysis or similar statistical functions. The error indicates that the R-squared (R²) value, which is a statistical measure of how well the regression predictions approximate the real data points, is outside the valid range of 0 to 1.
  Cause:
  
  Incorrect Data Input: The data being analyzed may contain errors, such as missing values, outliers, or incorrect data types that lead to an invalid calculation of R².
  Model Specification Issues: The model being used may not be appropriate for the data, leading to nonsensical results.
  Calculation Errors: There may be a bug or issue in the calculation logic within the SAP system or the specific module being used.
  
  Solution:
  
  
  Check Data Quality: 
  
  Review the dataset for any missing values, outliers, or incorrect data types. Clean the data as necessary.
  Ensure that the data is properly formatted and that all necessary variables are included in the analysis.
  
  
  
  Review Model Specification:
  
  Ensure that the model you are using is appropriate for the data. Consider whether the relationships you are trying to model are linear or if a different type of model is more suitable.
  If using multiple regression, check for multicollinearity or other issues that might affect the model's performance.
  
  
  
  Recalculate R²:
  
  After addressing any data quality or model specification issues, attempt to recalculate the R² value.
  If the error persists, consider using alternative statistical software or tools to verify the results.
  
  
  
  Consult Documentation:
  
  Review SAP documentation or help resources related to the specific module you are using for any known issues or additional troubleshooting steps.
  
  
  
  Seek Support:
  
  If you are unable to resolve the issue, consider reaching out to SAP support or your internal IT team for assistance. Provide them with details about the data, the model being used, and the steps taken to reproduce the error.
  
  
  
  Related Information:
  
  Understanding R²: R² values range from 0 to 1, where 0 indicates that the model does not explain any variability in the response data around its mean, and 1 indicates that it explains all the variability. Values outside this range are not valid.
  Statistical Analysis in SAP: Familiarize yourself with the statistical analysis tools available in SAP, such as SAP Predictive Analytics or SAP BW, and their specific requirements and limitations.
  Best Practices: Always validate your data and model assumptions before running statistical analyses to avoid errors and ensure meaningful results.
  
  By following these steps, you should be able to identify the cause of the error and implement a solution to resolve it.