The maximum likelihood analysis is widely performed in statistical and scientific analysis. This estimation is used for finding the likelihood of a certain parameter. The analysis can be performed with different approaches. In this article, we will perform maximum likelihood estimation in an Excel spreadsheet.

**Table of Contents**hide

## Download Practice Workbook

You can download and practice this workbook.

## Basics of Probability and Likelihood

**Probability**: Probability means the likelihood of an event to occur. It deals with the occurrence of future events. Probabilities do add up to be 1.

**Likelihood**: Likelihood refers to past events with known outcomes. Unlike probability Likelihood together doesnâ€™t add up to be 1.

## Steps to Find Maximum Likelihood Estimation in Excel

Finding maximum likelihood is generally a mathematical process. We are going to find the likelihood estimation by tossing a coin. For this analysis we are using a binomial formula which is:

^{n}C_{x}*P_{x}*(1-p)^{n-x}### Step 1: Assumption for Maximum Likelihood Estimation

Letâ€™s assume a coin has been tossed 20 times. The head comes up 8 times and the tail comes up 12 times.

On the basis of this information, we will find the likelihood of head for different probabilities.

And, thereby, will be able to find its maximum likelihood.

âž¤ Add two columns where you will insert different probability values in **Column E **and **Column F** for calculating the **Likelihood**.

We have to insert different probability values in the Probability** of Heads** column. The probability of any given field varies from 0% to 100%.

âž¤ So, insert the probability values ranging from **0%** to **100%** at an increment of **10%**.

Note: You can change the probability increment interval according to your requirement.

**Read More: ****How to Create Estimation Tool in Excel (with Easy Steps)**

### Step 2: Use of COMBIN Function for Maximum Likelihood Estimation

Now, we have to find the likelihood for each probability. For this, we will use theÂ **COMBIN** **function**.

The likelihood formula in binomial expression is given as:

`=`

^{n}`C`

_{x}`*P`

_{x}`*(1-p)`

^{n-x}In this equation,

**n**= No. of tosses**P**= probability of heads**x**= No. of heads

In the Excel sheet, we have to use this same formula.

âž¤ So, write the following formula in **F5** and press **ENTER**.

`=COMBIN($C$4,$C$5)*E5^$C$5*(1-E5)^$C$6`

Here,

**$C$4**= No. of Tosses**$C$5**= No. of Heads**$C$6**= No. of Tails**E5**= Probability of Heads(Relative)

Thereby, we get the likelihood of 0% probability which is also 0%.

âž¤ Now, **Hold** and **Drag** the** F5 **downward.

By doing so, we have got the** likelihood** of getting heads for all other **Probabilities**.

The above analysis means that the likelihood of getting heads is highest when the probability is 40% and the maximum likelihood is about 18% which occurs for the probability of 40%.

**Similar Readings**

**How to Do Interior Estimation in Excel (2 Suitable Methods)****Create a Project Time Estimation Sheet in Excel****How to Calculate Residential Construction Cost Estimator in Excel**

### Step 3: Using Chart to Show Result

Now, letâ€™s use the Excel chart to show the maximum likelihood estimation.

âž¤ Copy the two columns **Probability of Heads** and** Likelihood of Heads **in a different sheet and paste into suitable cells.

âž¤ After that, select the two columns and go to the **Insert** ribbon. Then, choose a suitable **Insert Scatter(X,Y) or Bubble Chart**.

And, thereby, you will get a chart on the basis of your previous result. Where the **Likelihood **values will be along the** Y-axis** and the **Probability** values will be along the **X-axis**.

From this chart, we can see that the maximum likelihood occurs at 40% probability and which is 17.97%.

## Practice Section

We are providing a practice workbook that you can download and practice yourself.

## Conclusion

So, thatâ€™s all for the article. We have shown you how to find the maximum likelihood estimation in Excel. Thank you for reaching out this far. We hope you find the content of this article useful. If there are any queries or suggestions, please do leave a comment.