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**Education Calculators** ▶ Covariance Calculator

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Our covariance calculator is a statistics tool that estimates the covariance between two random variables X and Y in probability & statistics experiments. Moreover, you need this covariance statistics calculator, if you want to:

- Calculate Covariance From Dataset
- Calculate Covariance From Correlation Coefficient
- Compute Covariance Matrix

In this article, you will learn about the covariance formula, how to calculate covariance, and much more that you need to know! Well, before knowing about this covariance calculator, let’s start with some basics.

In the term of statistics and mathematics, covariance definition elaborates as the measurement of the relationship between two random variables (X, Y). In simple words, covariance is said to be a measure of how much two random variables can vary together. Studies depict that the covariance concept is very similar to the variance concept. However, the statistical covariance tells you how two variables vary together while statistical variance tells you how a single variable varies. Well, calculating covariance becomes easy with the help of the above covariance calculator.

Our covariance calculator uses the given formulas while returning these results:

**Sample Covariance Formula:**

Sample Cov (X,Y) = Σ E((X-μ)E(Y-ν)) / n-1

- In the above covariance equation;
- X is said to be as a random variable
- E(X) = μ is said to be the expected value (the mean) of the random variable X
- E(Y) = v is said to be the expected value (the mean) of the random variable Y
- ‘n’ indicates the number of items in the data set

**Mean x:**

Mean x (x) = 1/n ∑_(i=1)^n▒xi

**Mean y:**

Mean x (x) = 1/n ∑_(i=1)^n▒yi

**Population Covariance Formula:**

Population COV (X,Y) = Σ E((X-μ)E(Y-ν)) / n

The covariance calculator is the tool that finds out the statistical relationship between the two sets of population data (X and Y). Also, this covariance tool allows you to calculate covariance matrix and the covariance between two variables X and Y for a given correlation coefficient (Pearson’s) and standard deviations. Don’t fret; covariance calculation is quite easy with this advanced covariance statistics tool.

Read on!

Out statistics calculator is quite easy to use, you just have to follow the given steps:

**Input:**

- First of all, you have to choose the option from which you want to calculate covariance, here you ought to select ‘dataset’
- Then, you have to enter the dataset of X into the designated box
- Very next, you ought to enter the dataset of Y into the designated box

**Output:**

Once you added the above values, hit the calculate button, the calculator shows:

- Set X
- Set Y
- Number of Samples
- Mean X̄
- Mean Ȳ
- Sample Covariance
- Population Covariance

**Input:**

- First, you ought to select the option from which you want to calculate covariance, here select ‘from a correlation option’
- Then, you have to enter the value of correlation between X and Y
- Now, you have to enter the standard deviation for X
- Very next, you have to enter the standard deviation for Y

**Output:**

- Once you added all the above values, then hit the calculate button, this covariance calculator computes covariance value between two variables X and Y

**Input:**

- First, choose the option of ‘Covariance Matrix’ from the drop-down menu of this covariance matrix calculator
- Very next, you ought to input the matrix into the designated box

**Output:**

- Once enter the above value, then hit the calculate button, our covariance matrix calculator shows the covariance matrix

Let’s take a look at covariance example:

Suppose that you want to find the covariance of the following set:

X = 2.1, 2.5, 3.6, 4.0 (mean = 3.1)

Y = 8, 10, 12, 14 (mean = 11)

Here, you have to use the above covariance equation that is:

Cov (X,Y) = ΣE((X-μ)(Y-ν)) / n-1

Cov(X,Y) = (2.1-3.1)(8-11)+(2.5-3.1)(10-11)+(3.6-3.1)(12-11)+(4.0-3.1)(14-11) /(4-1)

Cov(X,Y) = (-1)(-3) + (-0.6)(-1)+(.5)(1)+(0.9)(3) / 3

Cov(X,Y) = 3 + 0.6 + .5 + 2.7 / 3

Cov(X,Y) = 6.8/3

Cov(X,Y) = 2.267

However, from this example you got a positive covariance, it means that the variables are positively related.

**Note:**

If you see the given denominator of the above covariance formula, you have the degrees of confidence. However, in the above covariance example, we had more than 2 terms, thus we used the formula n – 1. When you are going to find the covariance of two random variables, then you ought to divide the formula by n only.

From the above example of covariance you will come to know, if you had a positive covariance, which means there is a positive relationship between the variables or that said they are positively related. However, you can use our covariance calculator to calculate covariance from correlation.

As a rule of thumb, a large covariance indicates that there may be a strong relationship between variables. Nevertheless, remember that you can’t compare variances over data sets that have several scales. You just have to think about comparing two datasets of variables where one is expressed in inches and the other one in pounds.

This is the problem with the interpretation of covariance outcomes, so as a far better approach is to account the correlation coefficient. So, you have to use the following formula instead:

**Corr(X,Y) = Cov(X,Y) / σXσY**

However, you can confirm your outcomes in our calculate covariance from correlation.

Let’s start with covariance:

- Covariance is something that indicates the measurement between two random variables X and Y
- Covariance is a measurement of correlation
- Values of covariance exist between –x and +x
- Change in scale will affects the value of the covariance
- Remember that covariance always has units

Now, ahead to Correlation:

- Correlation is something that indicates the measurement that how strongly two variables are related
- Correlation is said to be a scaled version of covariance
- Values of correlation exist between -1 and +1
- Change in scale does not affect the value of the correlation
- Remember that correlation does not have units

The Correlation Coefficient has a different number of advantages over covariance for computing strengths of relationships, these are:

- Covariance can account on practically any number while a correlation is limited: -1 to +1
- Correlation is more useful for finding how strong the relationship is between the two variables, just because of its numerical limitations
- Correlation is not at all affected by changes in the center that is said to be the mean or scale of the variable

Unlike variance, which is non-negative, Covariance is something that can be negative or positive (or zero, of course). A positive covariance indicates that two random variables tend to vary in the same direction; a negative variance indicates that they vary in opposite directions, and zero means they don’t vary together.

The symbol for covariance is cov(X, Y).

When it comes to covariance, there is no minimum or maximum value, that’s why the values are more difficult to interpret. For instance, a covariance of 50 may indicate a strong or weak relationship as this actually depends on the units in which covariance is measured.

Covariance values are not standardized, according to statistical terms, the covariance can range from negative infinity to positive infinity. Thus, the value for a perfect linear relationship all depends on the data.

When it comes to compare data samples from different populations, the covariance (COV) is considered to find how much two random variables vary together. And, correlation is something that accounts to find when a change in one variable can result in a change in another. Remember that both covariance and correlation determine linear relationships between variables.

Well, when it comes to comparison, which is a better measure of the relationship between two variables, correlation is preferred over covariance as it is the measure that remains unaffected by the change in location and scale – and, also can be accounted to make a comparison between two pairs of variables.

Variance is the mathematical term used in statistics and probability theory, it is referred to the spread of a dataset around its mean value.

Sometimes the covariance is said to be a measure of ‘linear dependence’ between the two random variables. That does not mean the same thing that is in the context of linear algebra.

Covariance is said to be a statistical tool that is taken into account to find out the relationship between the movements of two asset prices. When two stocks tend to move together, they are having a positive covariance, while they move inversely, the covariance is negative

In modern portfolio theory, a covariance is a significant tool that is used to ascertain what securities to put in a portfolio.

However, risk and even the volatility can be reduced in portfolio by pairing assets, which have a negative covariance. No doubt, there are several theories behind this measuring factor, use the above covariance calculator to estimates the covariance between two random variables X and Y!

From Wikipedia, the free encyclopedia – For a broader coverage of this topic, see Covariance and Correlation Coefficient

From dummies – Business Statistics By Alan Anderson – how to calculate Covariance and Correlation of Data Samples

An initiative by IIT IIM Graduates, EDUCBA (Corporate Bridge Consultancy Pvt Ltd) – Covariance Formula with Example – Population Covariance Formula & Sample Covariance Formula

From XPLAIND – The Statistics Correlation Coefficient Term – Formula for covariance