What is chi square in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: chi square

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Divergence measure between cluster distribution and population distribution for this dimension. More precisely, its value represents the degree of confidence you can have in stating that the two distributions are significantly different. It does not provide a measure of how far these distributions are from each other. Instead, it allows us to be sure that the two distributions are different when we get a value near to 1.

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• Key Concepts: 
  Chi-square is a statistical test used to determine if there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. It is used to compare two or more categorical variables in a dataset. It is also used to test the independence of two variables. 
  
  How to use it: 
  In SAP Predictive Analytics, chi-square can be used to determine if there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. It can also be used to test the independence of two variables. To use chi-square, you must first select the data set that you want to analyze and then select the appropriate statistical test. Once you have selected the test, you will need to enter the parameters for the test, such as the number of categories and the expected frequencies. Once you have entered all of the parameters, you can run the test and view the results. 
  
  Tips & Tricks: 
  When using chi-square, it is important to remember that it is only valid when there are at least five observations in each category. Additionally, it is important to ensure that all of the data points are independent of each other. Finally, it is important to remember that chi-square tests are only valid when there are no ties between categories. 
  
  Related Information: 
  Chi-square is related to other statistical tests such as ANOVA and t-tests. Additionally, it can be used in conjunction with other predictive analytics techniques such as regression analysis and decision trees.