# Relationship Between Variables

In Data Science, very frequently you have to determine the**strength of the association**of two or more variables. Statistics can be performed using a

**single variable**(Univarate statistics) ,

**two variables**(Bivariate statistics) , and even

**multiple variables**(Multivariate statistics).

## Univarate Statistics

When you use a single variable to perform analysis, where most of the**descriptive statistics**lie, such statistics are called univariate statistics.

## Bivariate Statistics

When you use two variables to perform analysis, which is generally the case with**inferential statistics**where you are trying to assess the relationship between the two samples, this statistic is commonly called a Bivariate Statistics.

## Multivariate Statistics

When you have multiple variables where you**simultaneously assess**the relationship using multiple variables, this is known as multivariate statistics.

## Correlation

Correlation is a**statistical technique**that can perform whether and how strongly pairs of variables are related. If the two variables move in the

**same direction**, then those variables are said to have a positive correlation. If they move in

**opposite directions**, then they have a negative correlation.

## Correlation Coefficient

The Correlation Coefficient is a statistical measure of the strength of the**linear association**between the relative movements of two variables. The

**correlation coefficient**always takes a value between -1 and 1.

**1**indicates a strong positive relationship.**-1**indicates a strong negative relationship.**0**indicates no relationship at all.

## Covariance

In statistics, a**Covariance**refers to the measure of how two random variables will change when they are compared to each other. In other words, it defines the changes between the

**two variables**, such that change in one variable is equal to change in another variable. Unlike the

**correlation coefficient**, covariance is measured in units. The units are computed by multiplying the units of the two variables.

## Causation

Causation indicates a relationship between**two events**where one event is affected by the other. It explicitly applies to cases where action A causes outcome B i.e. there is a causal relationship between the two events. This is also referred to as

**cause and effect**. For example: when the value of one event, or variable, increases or decreases as a result of other events, it is said there is

**causation**.

## Pearson's Correlation

Pearson correlation coefficient**"r"**is defined in statistics as the measurement of the strength of the

**linear relationship**or association between two

**continuous variables**and their association with each other. The Pearson correlation method assigns a value between - 1 and 1, where 0 is no correlation, 1 is total positive correlation, and - 1 is total negative correlation.

**Pearson correlations**are only suitable for quantitative variables (including dichotomous variables).

## Spearman's Correlation

Spearman's correlation is a statistical measure of the**strength and direction**of association that exists between two variables measured on at least an

**ordinal scale**.

**Related Topics**