Both the Correlation and Regression are the analysis that is based on multivariate distributions. Any multivariate distribution is described or referred to be as a distribution where multiple variables are used. Correlation is the analysis which lets or helps us to know the association or the absence of the relationship between two quantitative variables. On the other side of the story, Regression analysis, predicts the value of the variable which is dependent based on the known value of the provided independent variable. This is generally done by assuming the average mathematical relationship that is there between the two or more variables that are connected to each other.

In general, correlation is defined as the measure of the linear relationship between any of the two quantitative variables such as height and weight. Correlation, in simple terms, means that there is some or the other type of relationship between any of the two quantitative variables. There are three types of Correlation, such as:

- Positive Correlation – In such a case where the values of one of the quantitative variable increase respectively as the values of the other quantitative variable increase, in that case, this is known as a positive correlation. It has a direct relationship.
- Negative Correlation – And unlike it when the values of one of the quantitative variables decrease as the value of another quantitative variable increases in order to form an inverse relationship between the two, this is known as a negative correlation.
- Zero Correlation – Then comes the part of the zero correlation. If there is a change in one of the quantitative variable and it does not depend by a way to the other variable, then this type of correlation between the two variables is said to be a zero correlation.

Regression in the other hand is a statistical measurement which is generally used in the sectors of finance, investing and other disciplines. With the help of this, we are able to determine the strength of the relationship that is between one dependent variable and which is in correspondence with a series of other changing variables, that is usually referred to as the independent variables. There are various types of regressions that are generally classified based on their functionality, some of these regressions are:

- Simple linear Regression – In any type of statistical method that can help us to summarize and study the different types of relationships that are available between any two continuous variables, among which one of the variable is a Dependent variable and the other one is an Independent variable, is referred to as, a Simple linear regression.
- Multiple linear Regression – The linear relationships that examines the different types of available facts and figures between the two different types of variable. In this case there can be variables, one of which is the Dependent variable and there can be two or more types of Independent variables that need to get examined. This is called a multiple linear Regression.

With the help of Regression, in the sectors of investment and financial managers, we are able to add the valuable assets and understand the various types of the relationships between different variables, such as commodity prices and the stocks of businesses which are dealing in those commodities.

The basic **difference
between correlation and regression** is that the objective of correlation is to find the
numerical value that is expressing the relationship between any of the two
quantitative variables. But when it comes to regression, it is used to estimate
the values of the random variable on the basis or with respect to the values of
a fixed variable that is used. Correlation is such a statistical measurement
that helps us to determine the co-relationship or association of any two
quantitative variables. On the Regression is the stat that describes how any
independent variable is numerically related to the overhead dependent variable
of it. Correlation is to represent the linear relationship that is connecting
any two quantitative variables. But regression on the other line estimates any
one of the variables on the basis and with respect to another variable in
contact. In correlation, there is no difference between Dependent and
Independent variables but in regression both of the variables used are
different.