Calculates the pvalue of the statistical test for the population excess kurtosis (4th moment).
Syntax
TEST_XKURT(X, Return_type, Alpha)
X is the input data sample (one/two dimensional array of cells (e.g. rows or columns)).
Return_type is a switch to select the return output (1 = PValue (default), 2 = Test Stats, 3 = Critical Value.
Method  Description 

1  PValue 
2  Test Statistics (e.g. Zscore) 
3  Critical Value 
Alpha is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.
Remarks
 The data sample may include missing values (e.g. #N/A).
 The test hypothesis for the population excess kurtosis:
$$H_{o}: K=0$$
$$H_{1}: K\neq 0$$
Where:
 $H_{o}$ is the null hypothesis.
 $H_{1}$ is the alternate hypothesis.
 For the case in which the underlying population distribution is normal, the sample excess kurtosis also has a normal sampling distribution:
$$\hat K \sim N(0,\frac{24}{T}) $$
Where:
 $\hat k$ is the sample excess kurtosis (i.e. 4th moment).
 $T$ is the number of nonmissing values in the data sample.
 $N(.)$ is the normal (i.e. gaussian) probability distribution function.
 Using a given data sample, the sample excess kurtosis is calculated as:
$$\hat K (x)= \frac{\sum_{t=1}^T(x_t\bar x)^4}{(T1)\hat \sigma^4}3$$
Where:
 $\hat K(x) $ is the sample excess kurtosis.
 $x_i $ is the ith nonmissing value in the data sample.
 $T$ is the number of nonmissing values in the data sample.
 $\hat \sigma$ is the sample standard deviation.
 The underlying population distribution is assumed normal (gaussian).
 This is a twosides (i.e. twotails) test, so the computed pvalue should be compared with half of the significance level ($\frac{\alpha}{2}$).
Examples
Example 1:


Formula  Description (Result)  

=KURT($B$2:$B$20)  Sample excess kurtosis (1.0517)  
=TEST_XKURT($B$2:$B$20)  pvalue of the test when excess kurtosis = 0 (0.171) 
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