Financial Modeling Tutorials

- Financial Modeling Basics
- Excel Modeling
- Financial Functions in Excel
- Sensitivity Analysis in Excel
- Sensitivity Analysis
- Capital Budgeting Techniques
- Time Value of Money
- Future Value Formula
- Present Value Factor
- Perpetuity Formula
- Present Value vs Future Value
- Annuity vs Pension
- Present Value of an Annuity
- Doubling Time Formula
- Annuity Formula
- Annuity vs Perpetuity
- Annuity vs Lump Sum
- Deferred Annuity Formula
- Internal Rate of Return (IRR)
- IRR Examples (Internal Rate of Return)
- NPV vs XNPV
- NPV vs IRR
- NPV Formula
- NPV Profile
- NPV Examples
- PV vs NPV
- IRR vs ROI
- Break Even Point
- Payback Period & Discounted Payback Period
- Payback period Formula
- Discounted Payback Period Formula
- Profitability Index
- Cash Burn Rate
- Simple Interest
- Simple Interest vs Compound Interest
- Simple Interest Formula
- CAGR Formula (Compounded Annual Growth Rate)
- Effective Interest Rate
- Loan Amortization Schedule
- Mortgage Formula
- Loan Principal Amount
- Interest Rate Formula
- Rate of Return Formula
- Effective Annual Rate
- Effective Annual Rate Formula (EAR)
- Daily Compound Interest
- Monthly Compound Interest Formula
- Discount Rate vs Interest Rate
- Rule of 72
- Geometric Mean Return
- Real Rate of Return Formula
- Continuous compounding Formula
- Weighted average Formula
- Average Formula
- Average Rate of Return Formula
- Mean Formula
- Mean Examples
- Population Mean Formula
- Weighted Mean Formula
- Harmonic Mean Formula
- Median Formula in Statistics
- Range Formula
- Outlier Formula
- Decile Formula
- Midrange Formula
- Quartile Deviation
- Expected Value Formula
- Exponential Growth Formula
- Margin of Error Formula
- Decrease Percentage Formula
- Percent Error Formula
- Holding Period Return Formula
- Cost Benefit Analysis
- Cost Benefit Analysis Examples
- Cost Volume Profit Analysis
- Opportunity Cost Formula
- Opportunity Cost Examples
- Mortgage APR vs Interest Rate
- Normal Distribution Formula
- Standard Normal Distribution Formula
- Normalization Formula
- Bell Curve
- T Distribution Formula
- Regression Formula
- Regression Analysis Formula
- Multiple Regression Formula
- Correlation Coefficient Formula
- Correlation Formula
- Population Variance Formula
- Covariance Formula
- Coefficient of Variation Formula
- Sample Standard Deviation Formula
- Relative Standard Deviation Formula
- Standard Deviation Formula
- Volatility Formula
- Binomial Distribution Formula
- Quartile Formula
- P Value Formula
- Skewness Formula
- R Squared Formula
- Adjusted R Squared
- Regression vs ANOVA
- Z Test Formula
- F-Test Formula
- Quantitative Research

Related Courses

**Skewness Formula (Table of Contents)**

## What is Skewness Formula?

The term “skewness” refers to the statistical metric that is used to measure the asymmetry of a probability distribution of random variables about its own mean and its value can be positive, negative or undefined. The calculation of skewness equation is done on the basis of the mean of the distribution, the number of variables and the standard deviation of the distribution.

Mathematically, the skewness formula is represented as,

where

- X
_{i}= i^{th}Random Variable - X= Mean of the Distribution
- N = Number of Variables in the Distribution
- Ơ = Standard Distribution

### Explanation of the Skewness Formula

The calculation of skewness equation can be done by using the following steps:

**Step 1:** Firstly, form a data distribution of random variables and these variables are denoted by X_{i}.

**Step 2:** Next, figure out the number of variables available in the data distribution and it is denoted by N.

**Step 3:** Next, calculate the mean of the data distribution by dividing the sum of all the random variables of the data distribution by the number of variables in the distribution. The mean of the distribution is denoted by X.

**Step 4:** Next, determine the standard deviation of the distribution by using the deviations of each variable from the mean, i.e. X_{i} – X and the number of variables in the distribution. The standard deviation is calculated as shown below.

**Step 5:** Finally, the calculation of skewness formula is done on the basis of the deviations of each variable from the mean, number of variables and the standard deviation of the distribution as shown below.

4.9 (927 ratings)

### Example of Skewness Formula(with Excel Template)

**Let us take the example of a summer camp in which 20 students assigned certain jobs that they performed to earn money to raise fund for a school picnic. However, different students earned a different amount of money. Based on the information given below, determine the skewness in the income distribution among the students during the summer camp.**

**Solution:**

The following is the data for the calculation of skewness.

Number of variables, n = 2 + 3 + 5 + 6 + 4= 20

Let us calculate the midpoint of each of the intervals

- ($0 + $50) / 2 = $25
- ($50 + $100) / 2 = $75
- ($100 + $150) / 2 = $125
- ($150 + $200) / 2 = $175
- ($200 + $250) / 2 = $225

**Mean**

Now, the mean of the distribution can be calculated as,

Mean= ($25 * 2 + $75 * 3 + $125 * 5 + $175 * 6 + $225 * 4) / 20

**Mean** = **$142.50**

The squares of the deviations of each variable can be calculated as below,

- ($25 – $142.5)
^{2}= 13806.25 - ($75 – $142.5)
^{2}= 4556.25 - ($125 – $142.5)
^{2}= 306.25 - ($175 – $142.5)
^{2}= 1056.25 - ($225 – $142.5)
^{2}= 6806.25

**Standard Deviation**

Now, the standard deviation can be calculated by using the below formula as,

ơ = [(13806.25 * 2 + 4556.25 * 3 + 306.25 * 5 + 1056.25 * 6 + 6806.25 * 4) / 20]^{1/2}

**ơ** = **61.80**

The cubes of the deviations of each variable can be calculated as below,

- ($25 – $142.5)
^{3}= -1622234.4 - ($75 – $142.5)
^{3}= -307546.9 - ($125 – $142.5)
^{3}= -5359.4 - ($175 – $142.5)
^{3}= 34328.1 - ($225 – $142.5)
^{3}= 561515.6

**Calculation of Skewness**

Therefore, Calculation of skewness of the distribution will be as follows,

= (-1622234.4 * 2 + -307546.9 * 3 + -5359.4 * 5 + 34328.1 * 6 + 561515.6 * 4) /[ (20 – 1) * (61.80)^{3}]

**Skewness will be –**

**Skewness **= **-0.39**

Therefore, the skewness of the distribution is -0.39 which indicates that the data distribution is approximately symmetrical.

### Relevance and Use

As seen already in this article, skewness is used to describe or estimate the symmetry of data distribution. It is very important from the perspective of risk management, portfolio management, trading and option pricing. The measure is called “Skewness” because the plotted graph gives a skewed display. A positive skew indicates that the extreme variables are larger that skews the data distribution is such a way it escalates the mean value in a way that it will be larger than the median resulting in a skewed data set. On the other hand, a negative skew indicates that the extreme variables are smaller which brings down the mean value which results in a median larger than the mean. So, skewness ascertains the lack of symmetry or the extent of asymmetry.

### Recommended Articles

This has been a guide to Skewness Formula. Here we discuss how to calculate skewness with practical example and downloadable excel template. You can learn more about excel modeling from the following articles –

- Explanation of Normal Distribution Formula
- Explanation of the Standard Deviation Formula
- Examples of Standard Normal Distribution Formula
- Excel Standard Deviation Formula
- Formula of Covariance
- Frequency Distribution Excel
- Create a Normal Distribution Graph Excel
- Excel Lognormal Distribution

- 35+ Courses
- 120+ Hours of Videos
- Full Lifetime Access
- Certificate of Completion

- Basic Microsoft Excel Training
- MS Excel 2010 Training Course: Advanced
- Microsoft Excel Basic Training
- Microsoft Excel 2013 – Advanced
- Microsoft Excel 2016 – Beginners
- Microsoft Excel 2016 – Advanced