# How To Calculate Kurtosis In Excel

Understanding the kurtosis of a dataset can provide valuable insights into its distribution, particularly the presence of outliers and the 'tailedness' of the data. Calculating kurtosis in Excel involves using statistical functions and can be a multi-step process that requires attention to detail.

This guide offers a straightforward approach to computing kurtosis with Excel and introduces Sourcetable as a more user-friendly alternative for statistical analysis.

## Calculate Kurtosis in Excel

### Using the KURT Function

Excel's KURT function computes kurtosis for a dataset, indicating its peakedness or flatness compared to a normal distribution. Positive kurtosis values denote a sharper peak, while negative values suggest a flatter shape. The function handles up to 255 inputs, which can be numerical values, names, arrays, or cell references.

### Steps for Calculation

To calculate kurtosis in Excel, input your dataset into a column or row. Click on an empty cell where you want the kurtosis value to appear. Type =KURT() and select the range containing your data inside the parentheses. Press Enter to view the result.

### Interpreting Kurtosis Values

A kurtosis value greater than zero implies a distribution with a sharper peak than a normal distribution. Conversely, a value less than zero indicates a flatter distribution. The magnitude of the kurtosis value reflects the degree of peakedness or flatness.

### Calculating Excess Kurtosis

To find excess kurtosis, which is the kurtosis relative to a normal distribution, subtract 3 from the kurtosis value obtained. A positive excess kurtosis indicates a distribution with more pronounced tails, while a negative value suggests fewer extreme values than a normal distribution.

## Common Use Cases

• Analyzing the peakedness of stock return distributions for financial modeling

• Assessing the normality of data in psychological research studies

• Evaluating the tail-heaviness of climate-related data sets

• Improving quality control by analyzing the shape of production data distributions

• Conducting statistical data analysis for academic research in economics

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