Financial Modeling Tutorials

- Financial Modeling Basics
- Excel Modeling
- Financial Functions in Excel
- Sensitivity Analysis in Excel
- 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
- Internal Rate of Return (IRR)
- NPV vs XNPV
- NPV vs IRR
- NPV Formula
- 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
- Weighted Mean Formula
- Harmonic Mean Formula
- Median Formula in Statistics
- Range Formula
- Expected Value Formula
- Exponential Growth Formula
- Margin of Error Formula
- Decrease Percentage Formula
- Percent Error Formula
- Holding Period Return Formula
- Cost Benefit Analysis
- Cost Volume Profit Analysis
- Opportunity Cost Formula
- Mortgage APR vs Interest Rate
- Regression Formula
- Correlation Coefficient Formula
- Covariance Formula
- Coefficient of Variation Formula
- Sample Standard Deviation Formula
- Relative Standard Deviation Formula
- Volatility Formula
- Binomial Distribution Formula
- Quartile Formula
- P Value Formula
- Skewness Formula
- Regression vs ANOVA

## What is the Correlation Coefficient Formula?

The correlation coefficient formula is used in statistics mainly to analyze the strength of the relationship between the variables that are under consideration and further it also measures if there is any linear relationship between the given sets of data and how well they could be related. One of the common measures that are used in correlation is the Pearson Correlation.

The correlation values can range from -1.0 to 1.0, where -1.0 represents negative correlation and +1.0 represents positive relationship. It considers the relative movements in the variables and then defines if there is any relationship between them.

Below is the Correlation Coefficient formula:

Where

- r = correlation coefficient
- n = number of observations
- x = 1
^{st}variable in the context - y = 2
^{nd}variable

### Explanation of the Correlation Coefficient formula

If there is any correlation or say the relationship between two variables then it shall indicate if one of the variable changes in value, then the other variable will also tend to change in value say in specific which could be either in same or in opposite direction. The numerator part of the formula conducts a test and relative strength of the variables moving together and the denominator part of the formula scales the numerator by multiplying the differences of the variables from squared variables.

**Examples of Correlation Coefficient Formula (with Excel Template)**

Let’s see some simple to advanced examples of correlation coefficient formula to understand it better.

#### Correlation Coefficient Formula Example#1

**Consider the following two variables x andy, you are required to calculate the correlation coefficient.**

Below is given data for calculation of correlation coefficient.

**Solution:**

Using the above formula, we can calculate the correlation coefficient.

We have all the values in the above table with n = 4.

Let’s now input the values in the formula for the calculation of correlation coefficient.

4.9 (927 ratings)

Therefore, the calculation of the correlation coefficient is as follows,

r = ( 4 * 25,032.24 ) – ( 262.55 * 317.31 ) / √[(4 * 20,855.74) – (262.55)^{2}] * [(4 * 30,058.55) – (317.31)^{2}]

r = 16,820.21 / 16,831.57

**Coefficient will be –**

Coefficient = **0.99932640**

#### Correlation Coefficient Formula Example#2

**Country X is a growing economy country and it wants to conduct an independent analysis on the decisions taken by its central bank regarding interest rate changes whether those have impacted the inflation and has the central bank being able to control the same.**

Following the summary of interest rate and the inflation rate that prevailed in the country on an average for those years are given below.

Below is given data for calculation of correlation coefficient.

The President of the country has approached you to conduct analysis and provide a presentation on the same in the next meeting. Use correlation and determine whether the central bank has met its objective or not.

**Solution:**

Using the formula discussed above, we can calculate the correlation coefficient. Treating Interest rate as one variable say x and treating inflation rate as another variable as y.

We have all the values in the above table with n = 6.

Let’s now input the values in the formula for the calculation of correlation coefficient.

Therefore, the calculation of the correlation coefficient is as follows,

r = ( 6 * 170.91 ) – (46.35 * 22.24 ) / √[(6 * 361.19) – (46.35)^{2}] * [(6 * 82.74) – (22.24)^{2}]

r = -5.36 / 5.88

**Correlation will be –**

Correlation =** -0.92**

**Analysis:** It appears that the correlation between interest rate and inflation rate is in negative which appears to be correct relationship as interest rate rises inflation decreases which means they tend to move in opposite direction from each other and it appears from above result that central bank was successful in implementing the decision related to interest rate policy.

#### Correlation Coefficient Formula Example#3

**ABC laboratory is conducting research on height and age and wanted to know if there is any relationship between them. They have gathered a sample of 1000 people for each of the categories and came up with an average height in that group.**

Below is given data for calculation of correlation coefficient.

You are required to calculate the correlation coefficient and come up with the conclusion that if any relationship exists.

**Solution:**

Using the formula discussed above, we can calculate the correlation coefficient. Treating Age as one variable say x and treating height (in cms) as another variable as y.

We have all the values in the above table with n = 6.

Let’s now input the values in the formula for the calculation of correlation coefficient.

Therefore, the calculation of the correlation coefficient is as follows,

r =( 6 * 10,137 ) – (70 * 850) / √[(6 * 940 – (70)^{2}] * [(6 * 1,20,834) – (850)^{2}]

r= 1,322.00 / 1,361.23

**Correlation will be –**

Correlation = **0.971177099**

### Relevance and Use of Correlation Coefficient Formula

If a variable change in value and along with that other variable changes in value, then understanding that relationship is critical as one can use the value of the former variable to predict the change in a value of the latter variable. Correlation has many multiple usages today in this modern era like it is used in the financial industry, scientific research and where not. But however, it is important to know that correlation has major three types of relationships. First one is a positive relationship which states if there is the change in a value of a variable then there will be the change in the related variable in the same direction, similarly, if there is a negative relationship then the related variable will behave in opposite direction. Also, if there is no correlation then r will imply a zero value. See below images to better understand the concept.

### Recommended Articles

This has been a guide to Correlation Coefficient Formula. Here we discuss how to calculate the correlation coefficient using its formula along with examples and downloadable excel template. You can learn more about financing from the following articles –

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