WorksheetFunction.ChiSq_Test Method
Returns the test for independence.
Namespace: Microsoft.Office.Interop.Excel
Assembly: Microsoft.Office.Interop.Excel (in Microsoft.Office.Interop.Excel.dll)
Syntax
'Declaration
Function ChiSq_Test ( _
Arg1 As Object, _
Arg2 As Object _
) As Double
'Usage
Dim instance As WorksheetFunction
Dim Arg1 As Object
Dim Arg2 As Object
Dim returnValue As Double
returnValue = instance.ChiSq_Test(Arg1, _
Arg2)
double ChiSq_Test(
Object Arg1,
Object Arg2
)
Parameters
Arg1
Type: System.ObjectThe range of data that contains observations to test against expected values.
Arg2
Type: System.ObjectThe range of data that contains the ratio of the product of row totals and column totals to the grand total.
Return Value
Type: System.Double
Remarks
ChiSq_Test returns the value from the chi-squared (χ2) distribution for the statistic and the appropriate degrees of freedom. You can use χ2 tests to determine whether hypothesized results are verified by an experiment.
If actual_range and expected_range have a different number of data points, ChiSq_Test returns the #N/A error value.
The χ2 test first calculates a χ2 statistic using the formula:
Figure 1: Formula for x squared test
.jpg "Formula for x squared test")
where:
Aij = actual frequency in the i-th row, j-th column
Eij = expected frequency in the i-th row, j-th column
r = number or rows
c = number of columns
A low value of χ2 is an indicator of independence. As can be seen from the formula, χ2 is always positive or 0, and is 0 only if Aij = Eij for every i,j.
ChiSq_Test returns the probability that a value of the χ2 statistic at least as high as the value calculated by the above formula could have happened by chance under the assumption of independence. In computing this probability, ChiSq_Test uses the χ2 distribution with an appropriate number of degrees of freedom, df. If r > 1 and c > 1, then df = (r - 1)(c - 1). If r = 1 and c > 1, then df = c - 1 or if r > 1 and c = 1, then df = r - 1. r = c= 1 is not allowed and generates an error.
Use of ChiSq_Test is most appropriate when Eij’s are not too small. Some statisticians suggest that each Eij should be greater than or equal to 5.