How To Fix TRM_REGR_TOOL1009 - Standard error is zero; T stat. computation is not possible

SAP Error Message - Details

• Message type: E = Error

• Message class: TRM_REGR_TOOL1 - Messages for Regression Tool

• Message number: 009

• Message text: Standard error is zero; T stat. computation is not possible

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 TRM_REGR_TOOL1009 - Standard error is zero; T stat. computation is not possible ?

  The SAP error message TRM_REGR_TOOL1009 indicates that there is an issue with the statistical computation of the T-statistic in the context of regression analysis. The specific message "Standard error is zero; T stat. computation is not possible" suggests that the regression model has encountered a situation where the standard error of the regression coefficients is zero. This typically occurs when there is insufficient variability in the data or when the model is not properly specified.
  Causes:
  
  Insufficient Data Variability: If the dependent variable does not vary (e.g., all values are the same), the standard error cannot be computed.
  Perfect Multicollinearity: If one or more independent variables are perfectly correlated with each other, it can lead to a situation where the model cannot estimate the coefficients properly.
  Too Few Data Points: If there are not enough observations relative to the number of predictors, it can lead to issues in estimating the standard error.
  Incorrect Model Specification: If the model is incorrectly specified (e.g., missing important variables or including irrelevant ones), it can lead to problems in estimation.
  
  Solutions:
  
  Check Data Variability: Ensure that the dependent variable has sufficient variability. If it does not, consider using a different dataset or modifying the existing one.
  Examine Multicollinearity: Use correlation matrices or variance inflation factors (VIF) to check for multicollinearity among independent variables. If multicollinearity is present, consider removing or combining correlated variables.
  Increase Sample Size: If possible, increase the number of observations in your dataset to provide more information for the regression analysis.
  Review Model Specification: Reassess the model to ensure that it is correctly specified. This may involve adding or removing variables based on theoretical considerations or exploratory data analysis.
  Use Regularization Techniques: If multicollinearity is an issue, consider using regularization techniques such as Ridge or Lasso regression, which can help in dealing with multicollinearity.
  
  Related Information:
  
  Regression Analysis: Understanding the fundamentals of regression analysis, including assumptions and diagnostics, can help in troubleshooting issues like this.
  Statistical Software Documentation: Refer to SAP documentation or help resources for specific guidance on regression tools and error messages.
  Consulting with a Statistician: If the problem persists, it may be beneficial to consult with a statistician or data analyst who can provide insights into the data and model.
  
  By addressing the underlying causes of the error, you should be able to resolve the issue and successfully compute the T-statistic for your regression analysis in SAP.