What is R square in SAP SCM-APO-FCS - Demand Planning?

SAP Term: R square

• Component: SCM-APO-FCS

• Component Name: Demand Planning

• Description: In multiple linear regression, the indicator of how well a particular combination of X variables the model drivers explains the variation in Y the dependent variable. R square ranges in value from 0 to 1. A value of 0 means that the multiple linear regression model does nothing to explain the variation in Y. A value of 1 means that the model is a perfect fit. A value of 0.75 or more indicates an acceptable model. R square is also known as the coefficient of determination or measure of goodness-of-fit. There are two points to note when using R square: R square is a nondescending function of the number of explanatory variables present in the model; that is, as you add more historical data and as you add more explanatory variables X's, R square almost always increases and never decreases. This is because the addition of explanatory variables to the model causes prediction errors to be small. R square assumes that the data set being analyzed is the entire population. In fact, t

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• Key Concepts: 
  R square is a statistical measure used in Demand Planning in SAP SCM-APO-FCS. It measures the correlation between the actual and forecasted values of a given item. The closer the R square value is to 1, the better the forecast accuracy. 
  
  How to use it: 
  R square can be used to evaluate the accuracy of a forecast. To calculate R square, you need to compare the actual values of an item with its forecasted values. The formula for calculating R square is (1 - (sum of squared errors/sum of squared total)). 
  
  Tips & Tricks: 
  When using R square to evaluate forecast accuracy, it is important to remember that a higher R square value does not necessarily mean that the forecast is more accurate. It simply means that there is a stronger correlation between the actual and forecasted values. 
  
  Related Information: 
  R square can be used in conjunction with other metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE) to get a more comprehensive view of forecast accuracy. Additionally, it can be used to compare different forecasting models and determine which one is more accurate.