In Excel or Google Sheets, kurtosis for the sample data is =KURT(C2:C31). It returns 8.52.
Excess kurtosis (sample-adjusted), as computed by Excel KURT and pandas .kurt()
Step by step
- Download daily.csv below and open it in Excel (File → Open) or Google Sheets (File → Import). Columns: A day, B ad_spend, C website_orders, D app_orders, E avg_basket; data in rows 2 to 31.
- Click an empty cell and enter =KURT(C2:C31).
- Check the result against 8.52. If it differs, check that the ranges cover rows 2 to 31 and that no cell holds text instead of a number.
The formula
=KURT(C2:C31)Expected result on the sample data: 8.52.
What the result means
One extreme day in thirty gives this series a high kurtosis. It is a flag to look at the tails directly, not a number to manage. In practice, plot a histogram or list the most extreme values: that shows what is in the tail, which a single kurtosis figure cannot.
Notes for Excel
- Google Sheets supports the same function names. In locales that use a comma as the decimal separator, separate arguments with semicolons.
- Excel functions skip blank cells and text but include zeros. A missing day typed as 0 changes the answer; leave it blank if it is unknown.
Where kurtosis misleads
- Excel and pandas report excess kurtosis (normal = 0); some tools report plain kurtosis (normal = 3).
- It is dominated by the most extreme values and is unstable in small samples.
- High kurtosis does not mean a "peaked" distribution; it is about the tails.
The plain-language explanation is on the Kurtosis page.
The data
| day | website_orders |
|---|---|
| 1 | 24 |
| 2 | 27 |
| 3 | 22 |
| 4 | 30 |
| 5 | 28 |
| 6 | 25 |
| 7 | 31 |
| 8 | 29 |
| 9 | 26 |
| 10 | 33 |
| 11 | 30 |
| 12 | 28 |
| 13 | 35 |
| 14 | 32 |
| 15 | 29 |
| 16 | 34 |
| 17 | 61 |
| 18 | 31 |
| 19 | 36 |
| 20 | 33 |
| 21 | 30 |
| 22 | 37 |
| 23 | 35 |
| 24 | 32 |
| 25 | 38 |
| 26 | 36 |
| 27 | 34 |
| 28 | 40 |
| 29 | 37 |
| 30 | 39 |
Same method, other tools
Related methods
Learn more about Excel
About the data on this page
The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.
To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.
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