Chi Square Test

The Chi-Square test is located under Model Studio > Statistical analysis > Hypothesis Test > Non Parametric Test.
Alternatively, use the search bar to find the Chi-Square test feature. Use the drag-and-drop method or double-click to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Properties of Chi Square Test

The available properties of the Chi Square test are shown below.

The table below describes the different properties of the Chi Square Test.

Field	Description	Remark
Run	It allows you to run the node.	–
Explore	It allows you to explore the successfully executed node.	–
Vertical Ellipses	The available options are Run till node Run from node Publish as a model Publish code	-
Task Name	It is the name of the task selected on the workbook canvas.	You can click the text field to edit or modify the task's name. Space between words is not allowed in the Task Name.
Response	It allows you to select one categorical variable from Dataset.	Only categorical columns are displayed in this dropdown. Pick up one column from the drop-down. Please make sure the selected categorical variable has only two categories.
Independent Variable	It allows you to select one category from the dataset.	All data columns are displayed in this dropdown. Pick up one column from the drop-down
Level of Significance	It allows you to set the level of significance.	The default value is 0.05. You are allowed to modify this value. The Alpha value must be between 0 to 1. It cannot be 0 and 1.
Add result as a variable	It allows you to use the result in the variables	For more details, refer to Adding Result as a Variable.
Node Configuration	It allows you to select the instance of the AWS server to provide control over the execution of a task in a workbook or workflow.	For more details, refer to Worker Node Configuration.

Example of Chi Square Test

Consider a company employee's data with department, gender, age, and other personal data. As an HR manager, you want to find out whether the Gender distribution in each department is equal or not.

An input data snippet is displayed below.

We apply Chi-Square to the input data by selecting two independent columns. The chosen values are given below.

Property	Value
Task Name	Chi_Square_Test
Response	Gender
Independent Variable	Department
Alpha	0.05

The result page consists of the following sections.

Frequency Table

The frequency table displays the Response variable values in the row. Independent variable values in the column.
Observed Frequency and Expected Frequency are calculated for each value. Observed Frequency is the number of occurrences found in the sample. The Expected Frequency is calculated as
Expected Frequency = ((Row Total) * (Column Total)) / Total Number of Observations
Computation Table

The computation Table displays the Test Statistics.

• Independent Variable – in this example department
• Response – Gender
• Observed Frequency (O) – the number of occurrences for the gender, of the department.
• Expected Frequency (E) – calculated as ((Row Total) * (Column Total)) / Total Number of Observations
• Observed Frequency – Expected Frequency (O – E)
• (O – E) ^2
• (O – E) ^2 / E

Hypothesis Interpretation

It displays following

• Null Hypothesis
• Alternative Hypothesis
• Result Value – it consists of Alpha value, p value, Critical value read from Chi-square table, Calculated value for the sample
• Interpretation – Compare the p value with the Alpha value. In this example, the p value is equal to the Alpha value hence the hypothesis is rejected.
  

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