**Correlation Meaning**

The correlation formula is applied to understand the movement of two data points in relation to each other. In the world of finance and investing, the two data points are usually securities. It is closely associated with the diversification of a portfolio, a method that is used to mitigate risk and spread investments across different asset classes.

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The **correlation formula statistics **help investors and analysts compare a particular stock in relation to the index to see if the performance is correlated. However, it is important to understand that this metric does not show if A caused B or if it was influenced by an external factor. It is computed as a correlation coefficient that should range between -1.0 and +1.0.

### Correlation Formula Explained

Correlation is a statistical measure between two variables. One may define it as the change of quantity in one variable corresponding to a change in another. One can calculate it by summation of the product of the sum of the first variable minus the mean of the first variable into a sum of the second variable minus the mean of the second variable divided by the whole under the root of the product of the square of the first variable minus the mean of the first variable into the sum of the square of second variable minus the mean of the second variable.

The value of a **simple correlation formula** is limited between -1 and +1 and can be interpreted as follows:

**-1:**If it is -1, then variables are known as perfectly negatively correlated. That means if one variable is moving in one direction, another is moving in the opposite direction.**0:**That means the variable is not having any correlation.**+1:**If it is +1, then variables are known as perfectly positively correlated. Both variables are moving in positive directions.

A perfect correlation implies that the movement in one variable shall result in the exact same movement in the other variable too in the same direction. While there are various methods of calculating this metric, the most widely used method of calculation is the Pearson product-moment correlation.

However, in the modern day, investors, market experts, and analysts use the CORREL function in MS Excel to avoid doing complex calculations which can be time-consuming and have a high possibility of errors.

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**Formula**

If we have two variables, x, and y, then the correlation coefficientCorrelation CoefficientCorrelation Coefficient, sometimes known as cross-correlation coefficient, is a statistical measure used to evaluate the strength of a relationship between 2 variables. Its values range from -1.0 (negative correlation) to +1.0 (positive correlation). read more between 2 variables can be found as:

**Correlation Coefficient = ∑(x(i)- mean(x))*(y(i)-mean(y)) / √ (∑(x(i)-mean(x))**

^{2}* ∑(y(i)-mean(y))^{2})Where,

- x(i)= value of x in the sample
- Mean(x) = mean of all values of x
- y(i) = value of y in the sample
- Mean(y) = mean of all values of y

### Examples

Let us understand the **correlation formula statistics **with the help of a few examples as discussed below. These examples shall give us a practical overview of the concept and its related factors.

It is effortless to calculate the correlation in ExcelCorrelation In ExcelCORREL function is a statistical function in Excel. The CORREL formula finds out the coefficient between two variables and returns the coefficient of array1 and array2. The correlation coefficient determines the relationship between the two properties.read more. The syntax of the function used is as follows:

Correlation Coefficient = CORREL (array1, array2)

#### Example#1

**Let’s take the same example above for calculating correlation using Excel.**

**Solution:**

Below are the values of x and y:

The calculation is as follows.

Basis Excel formula = CORREL (array(x), array(y))

**Coefficient = +0.95**

Since this coefficient is near +1, x and y are highly positively correlated.

#### Example#2** **

**Correlation is mainly useful for analyzing companies’ stock prices and creating a stock portfolio based on that.**

**Let us determine the correlation of Apple stock with the Nasdaq index based on the last one-year stock performance. Apple is a US-based** multinational companyMultinational CompanyA multinational company (MNC) is defined as a business entity that operates in its country of origin and also has a branch abroad. The headquarter usually remains in one country, controlling and coordinating all the international branches.
read more **specializing in IT products such as iPod, iPad, Mac, etc.**

**Solution:**

Below is the monthly return of Apple and Nasdaq stocks for the last year:

Let’s now input the values –

Correlation Coefficient = ∑(x(i)- mean(x)).(y(i)-mean(y))/√ ∑(x(i)-mean(x)) ^^{2} ∑(y(i)-mean(y))^^{2}

Correlation between Apple and Nasdaq= 0.039/ (√0.0039)

**Coefficient =0.62**

Since the correlation between Apple and Nasdaq is positive. Hence, Apple positively correlates with Nasdaq

#### Example#3

**Let us now examine the correlation between Walmart and the Nasdaq index based on last year’s stock performance. Walmart is a US-based company which has a retail supermarket chain.**

**Solution:**

Below is the monthly performance between Walmart and Nasdaq for the last year:

Let’s now input the values in the formula: –

Correlation Coefficient = ∑(x(i)- mean(x)).(y(i)-mean(y))/√ ∑(x(i)-mean(x)) ^^{2} ∑(y(i)-mean(y))^^{2}

Therefore, the calculation is as follows:

Correlation between Walmart and Nasdaq= 0.0032/ (√0.0346*0.0219 )

**Coefficient =0.12**

We can see that Walmart and Nasdaq are positively correlated but not as much compared to Apple’s correlation with Nasdaq.

### Relevance and Use

Let us understand the relevance and uses of applying the **simple correlation formula **to security and gauging its movements in accordance with the other variable through the points below.

- A correlation coefficient is useful in establishing the linear relationship”Linear”Aread more between two variables.
- It measures how a variable will move compared to the movement of another variable. For example, the practical use of this coefficient is to find out the relationship between stock price movement with the overall market movement. Based on this analysis, a stock analyst will include the proportion of stocks to create an optimal portfolioOptimal PortfolioPortfolio optimization refers to the strategic process of making improvements or positive changes to the investor's portfolio containing various assets. The Sharpe ratio helps to analyze the returns from an optimal portfolio.read more with minimum risk. Also, it is useful in data science to find the relationship between 2 variables.
- Also, the correlation coefficient is used very highly for studying the construct validity of data in factor analysis. Furthermore, it is highly used in regression analysisRegression AnalysisRegression Analysis is a statistical approach for evaluating the relationship between 1 dependent variable & 1 or more independent variables. It is widely used in investing & financing sectors to improve the products & services further. read more to predict the values of dependent variables based on the relationship between dependent and independent variables.
- Finally, this equation is useful in quantitative analysis to determine the relationship between various variables. The basis of this relationship, if a variable is unrelated to other variables, it can be eliminated from the list.

**Correlation Vs Regression**

While both **correlation formula statistics **and regression are widely followed numbers for their individual importance, it is important to understand the differences in their fundamentals and implications to completely understand the concept. Let us do so through the comparison below.

**Correlation**

- It refers to the relationship between two or more variables.
- X and Y are not fixed variables, they are random in nature.
- The range of the correlation or relationship lies between -1.0 to +1.0.
- This metric gives very limited information after the relationship between the variables is established.
- Therefore, it is the study of the linear relationship of the variables.
- Its objective is to find the numerical value that can be a representation of the relationship between the variables.

**Regression**

- Regression measures the average relationship between two or more variables.
- Here X is a random variable, whereas, Y is a fixed variable.
- It is expressed as an absolute figure.
- It is used to determine or predict the value of the other variable based on the value of the given variable.
- It is the study of the linear and non-linear relationship between the variables.
- Its objective is to find or predict the values of a random variable based on the value of the fixed variable.

### Recommended Articles

This article is a guide to Correlation Formula. Here we explain how to calculate, examples, relevance, and uses, and compared it with regression. You can learn more about financing from the following articles: –

- Gini CoefficientGini CoefficientGini Coefficient or Gini Index is statistical dispersion depicting the income dispersions amongst the population of a country i.e. it represents the wealth inequalities of the citizens of a particular country. read more
- Adjusted R Squared FormulaAdjusted R Squared FormulaAdjusted R Squared refers to the statistical tool which helps the investors in measuring the extent of the variance of the variable which is dependent that can be explained with the independent variable and it considers the impact of only those independent variables which have an impact on the variation of the dependent variable.read more
- Correlation vs CovarianceCorrelation Vs CovarianceCovariance and Correlation are two terms which are exactly opposite to each other; both are used for statistics and regression analysis. Covariance reflects how two variables vary from each other, whereas correlation depicts the relationship between two variables.read more
- Negative CorrelationNegative CorrelationA negative correlation is an effective relationship between two variables in which the values of the dependent and independent variables move in opposite directions. For example, when an independent variable increases, the dependent variable decreases, and vice versa.read more