How to Do Kernel Density Estimation in Excel (with Detailed Steps)

Kernel Density Estimation is a form of probability distribution function used in statistics. This method is used for nonparametric statistical data. In this article, we will discuss how to perform kernel density estimation in Excel with proper illustrations and explanations.

What Is Kernel Density Estimation?

The kernel density estimation is the process of estimating the probability density function for random values. This process makes the curve edges smooth based on the weighted values. It depends on the smoothing parameter called bandwidth. The mathematical formula to calculate kernel density estimation is given below:


  • K is kernel
  • h is bandwidth
  • n is the number of data

The kernel density estimation process is also applicable to machine learning applications.

Perform Kernel Density Estimation in Excel: Step-by-Step Procedure

Here, we will discuss the whole process of applying kernel density estimation in Excel. When we work with any statistical operation, two data are available for consideration population and sample data. The population is the whole data of a system. However all-time population data is not available. For this, we mostly work with sample data. Here, we will consider the data of the share price of Microsoft. We have collected data from We will import historical stock prices into Excel from Yahoo Finance.

📌 Step 1: Collect Data for Kernel Density Estimation

  • Here, we insert data for the last 6 months of Microsoft. This data indicates the change of share price day to day in percentile.

Collect data for Kernel Density Estimation

We collected data from the mentioned website.

📌 Step 2: Sort the Collected Data Using Excel SORT Function

We will sort the raw data in this section. The SORT function will be used for this purpose.

  • First, we add a new row on the right side.

  • Then, put the following formula on cell D5.

Sort data for Kernel Density Estimation

Here, 1st argument is the range, 2nd argument is the index number, 3rd argument is the sorting order, and 4th argument for False for sorting based on row.

📌 Step 3: Add Supporting Data to Calculate Kernel Density

We need some other data to calculate Kernel density. We will calculate those data from the raw data.

  • First, add new rows for the supporting data.

  • To calculate the sample size, we will use the COUNT function. Put the following formula on cell H5.

Count the number of data for Kernel Density Estimation

  • Assume, the value of bandwidth (h) is 00%.

📌 Step 4: Calculate Kernel Density

Here, we need to create a formula to calculate Kernel density estimation in Excel. The formula for Kernel density estimation is mentioned here.

  • Apply the customized Excel formula on cell E5 and expand that to the last cell.

Calculate Kernel Density Estimation

📌 Step 5: Plot a Suitable Excel Graph of Kernel Density vs Return

We already calculated kernel density. Now, plot that into a graph.

  • Choose the range D5:E131.
  • Click on the Insert tab.
  • Now, choose the Scatter with Smooth Lines from the Charts group.

Create a graph for Kernel Density Estimation

  • Look at the graph.

We can see the Density Vs. Return graph. We get this graph for bandwidth 1.00%. This bandwidth is an assumption.

📌 Step 6: Compare the Kernel Density vs Return Graphs for Different Bandwidths

Previously, we told that the value of bandwidth is an assumption. Now, we will change the bandwidth to see the change in the graph.

  • Look at the image below.

Compare Kernel Density Estimation for different bandwidth

1st graph is for h=1.00%. The line is smooth. In the 2nd graph, h=0.01%, the line is a bit noisy. Increase the value of h in the 3rd graph and set that to 0.10%. See this is much better than the 2nd one. In the 4th graph, h=4.00%, and the appeared graph is close to a straight line. But this assumption may not be correct all the time.

📌 Additional Step: Calculate Bandwidth with Formula and See the Changes in Graph

There are multiple ways to calculate bandwidth (h), for Kernel density estimation. The most two popular methods are given below.

Scott’s Rule: This method is used for normal distribution. The formula to calculate the bandwidth is Scott’s rule is:

Silverman’s Rule: This method can be used for all kinds of distribution processes. It is more robust but does not work for complicated cases. The formula of bandwidth in Silverman’s Rule is:

i. Calculate Standard Deviation for Scott’s Rule

Here, we calculate the Standard Deviation using the STDEV.S function. Look at the following formula.

  • We get the standard deviation and will use this to calculate the bandwidth.
  • Now, apply the following formula on formula bandwidth (h).

Calculate Deviation for Kernel Density Estimation

See the graph has been changed.

ii. Apply Scott’s Rule to Calculate Bandwidth

  • We will apply the following formula for bandwidth based on Scott’s rule.

As the bandwidth changed, the graph also changed accordingly.

iii. Calculate IQR for Silverman’s Rule

The IQR stands for interquartile range. It is the measure of the middle fifty. We can mathematically present this as,

IQR = Q3-Q1  

This is the difference between the 3rd and 1st quarters.

  • Apply the following formula to cell H8.

IQR for Kernel Density Estimation

We will use this IQR to calculate bandwidth in Silverman’s Rule.

iv. Use Silverman’s Rule for Bandwidth

  • In this section, we will apply the following customized formula based on Silverman’s Rule to calculate the bandwidth.

Based on this bandwidth, the kernel density and graph also change.

Read More: How to Find Maximum Likelihood Estimation in Excel

Download Practice Workbook

Download this practice workbook to exercise while you are reading this article.


In this article, we described all the steps to calculate kernel density estimation in Excel with explanations. I hope this will satisfy your needs.

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Alok Paul
Alok Paul

Alok Paul has completed his B.Sc. in Electronics and Telecommunication Engineering from East West University. He has been working on the ExcelDemy project for more than 2 years. He has written 220+ articles and replied to numerous comments. He is experienced in Microsoft Office, especially in Excel. He also led some teams on Excel and VBA content development. He has a keen interest in Advanced Excel, Data analysis, Excel Pivot Table, Charts, and Dashboard. He loves to research... Read Full Bio

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