How to Create a Skewed Bell Curve in Excel (With Easy Steps)

This article illustrates how to create a skewed bell curve in excel. A bell curve depicts the normal probability distribution. This name comes from the shape of the curve. A bell curve will be perfectly symmetrical if you generate it from a normal distribution. But it can be skewed if it is generated from a skewed distribution. You will learn how to create a skewed bell curve by following this article. You will see what a skewed bell curve looks like in the picture below.

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What Is a Skewed Bell Curve?

Skewed bell curves are associated with skewed distribution. For a highly skewed bell curve, the skewness will be either greater than 1 or less than -1. For a mildly skewed bell curve, the skewness will be either between -1 and -0.5 or  0.5 and 1. If the skewness is between -0.5 and 0.5, then the bell curve will be almost symmetrical. There are mainly two types of bell curves: positively and negatively skewed bell curves.

Positively Skewed Bell Curve:

It is also known as a right-skewed bell curve. If Mean > Median > Mode for a dataset, then the bell curve will be positively skewed. A positively skewed bell curve has a long tail pointing towards the right.

Negatively Skewed Bell Curve:

A negatively skewed bell curve is also called a left-skewed bell curve. If Mean < Median < Mode, then the bell curve will be negatively skewed. A negatively skewed bell curve has a long tail pointing towards the left.

Steps to Create a Skewed Bell Curve in Excel

Assume you have the following dataset. It contains the mark sheets of a group of students. Now you want to create a bell curve to compare the results. Then follow the steps below.

Step 1: Generate a Summary Statistics

• First, select Data >> Data Analysis as follows.

• Then, select Descriptive Statistics and click OK.

• Next, enter D5:D20 (Physics) for Input Range. Then, mark the radio button for Columns. Next, select the radio button for Output Range. Now, enter E4 for the output range. After that, check the Summary Statistics. Then, click OK.

• After that, you will see the following result. You can see that the Skewness is 1.29 indicating a highly positively skewed dataset. We will need the Mean and Standard Deviation from this table to create the skewed bell curve.

Read More: How to Create a Bell Curve with Mean and Standard Deviation in Excel

Step 2: Create a Bin Range

• Now, enter the following formula in Cell F5.

• Then, apply the following formula in cell F6 and drag the fill handle icon up to cell F10.

Step 3: Generate a Histogram

• Next, select Data >> Data Analysis as earlier. Then, select Histogram and click OK.

• Now, enter C5:C20 and F6:F10 for the Input Range and the Bin Range Then, mark the radio button for the Output Range and enter G4 for the output range. Next, click on the OK button.

• After that, you will get the following result. Now select range G7:H11 from the histogram table.

Read More: How to Create a Histogram with Bell Curve in Excel (2 Suitable Examples)

Final Step: Insert Skewed Bell Curve

• Then, select Insert >> Insert Scatter (X, Y) or Bubble Chart >> Scatter with Smooth Lines as in the following image.

• Finally, you will get the desired skewed bell curve as shown in the following picture.

• Now, repeat the same procedures for the other set of data (Chemistry). You will get the following result.

• Then, insert a Scatter Chart with Smooth Lines as earlier. After that, you will get the following result.

Things to Remember

• You can also use the SKEW function in excel to find the skewness of a dataset. It is always better to know the skewness of a dataset before trying to create a skewed bell curve.
• Some formulas have relative and absolute references. You need to enter them properly to avoid any erroneous results.

Conclusion

Now you know how to create a skewed bell curve in excel. Do you have any further queries or suggestions? Then please use the comment section below for that. You can also visit our ExcelDemy blog to explore more about excel. Stay with us and keep learning.

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