Regression describes how an independent variable is numerically related to the dependent variable.Correlation is used to represent the linear relationship between two variables.

Dear Ajit kumar Roy, Please read well the difference given here through Quora. Its informative.

https://www.quora.com/What-is-the-difference-between-correlation-analysis-and-regression-analysis

A correlation is a relationship between two variables. Each variable represents a particular phenomenon, if one of the phenomena changes in a particular direction, the second changes in the direction of the first or in the opposite direction of the first.

And the change of the two phenomena in the same direction in the sense of the increase in the first offset by an increase in the second or vice versa in the first offset by a decrease in the second relationship is either positive or increasing (positive), although the increase in the first offset by a deficit in the second or vice versa in the first phenomenon offset by an increase in the second we say That the link is backward or decreasing (negative)

A regression is a method by which the value of one variable can be estimated by the value of the other variable by the regression equation. It has the following types:

Simple linear regression: The word "simple" means that the dependent variable Y depends on one independent independent variable X and the word "linear" means that the relationship between the variables Y and X is linear.

 Multiple regression: If the variable Y depends on more than one independent variable.

Nonlinear regression: if the relationship between variable Y and variables

Independent is non-linear, such as second-class or exponential.

From sciencedatacentral.com, Correlation is described as the analysis which lets us know the association or the absence of the relationship between two variables ‘x’ and ‘y’. On the other end, Regression analysis, predicts the value of the dependent variable based on the known value of the independent variable, assuming that average mathematical relationship between two or more variables.

Please see the following ResearchGate links.

https://www.researchgate.net/post/What_is_the_difference_between_correlation_and_regression

https://www.researchgate.net/post/Difference_between_correlation_and_regression_Conceptual_and_Practical_approaches

https://www.researchgate.net/post/Compare_and_contrast_correlation_with_regression

https://www.researchgate.net/post/What_is_the_difference_between_correlation_and_regression2

Correlation is a statistical measure which determines association of two variables (i.e. dependent and independent variables). Regression on other hand side describes how an independent variable is numerically related to dependent variable. In correlation, there is no difference between dependent and independent variables while in regression both variables are different. Correlation is used to represent linear relationship between two variables while regression is used to estimate one variable on the basis of another variable. The objective of correlation is to find a numerical value expressing the relationship between variables while the objective of regression is to estimate value of random variable on the basis of the values of fixed variable. Correlation coefficient indicates the extent to which two variables move together while regression indicates the impact of a unit change in the known variable (X) on the estimated variable (Y).

Please also have a look at this link.

https://www.quora.com/What-is-the-difference-between-regression-and-correlation

Hom Nath Chalise provided a link that answers the question correctly.

From correlation we can only get an index describing the linear relationship between two variables; in regression we can predict the relationship between more than two variables and can use it to identify which variables x can predict the outcome variable y.

CORRELATION

A simple relation between two or more variables is called as correlation. If the change in one variable effect the change in another variable, then the variables are said to be correlated. Example: Price and Demand of a certain commodity.

TYPES OF CORRELATION

There are 3 types of correlation depending on nature, they are as follows.

1. Positive Correlation: If both the variable deviate in the same direction, then it is said to be the Positive correlation.

Example: Income and Expenditure of the certain family.

2. Negative Correlation: If both the variables deviate in the opposite directions, then it is said to be the Negative correlation.

Example: Price and Demand of a commodity.

3. Zero Correlation: If the change in one variable does not depend on the another variable, then the correlation between these variables is said to be Zero Correlation.

Example: Heights of students and their marks.

METHODS OF CORRELATION

The following methods are commonly used for finding the Correlation Coefficient.

1. Karl Pearson’s correlation coefficient. 2. Spearman’s Rank correlation coefficient. 3. Least Squares Method.

REGRESSION

Regression literally means going back or stepping back towards the average. Regression analysis is a mathematical measure of an average relationship between two or more variables in terms of original units of data.

In regression analysis, there are two variables.The variable whose value is influenced it is called as “Dependent Variable” and the variable which influences the value of the other variable is called as “Independent Variable”.

Example: Controlling the supply of goods may affect the price of the good.

REGRESSION LINES

1. Regression line of X on Y 2. Regression line of Y on X

TYPES OF REGRESSION

There are various types of regressions based on their functionality, some of them are as follows.

1.Simple linear Regression: Simple linear regression is a statistical method that helps to summarize and study relationships between two continuous variables: one Dependent variable and one Independent variable.

2.Multiple linear Regression: Multiple linear regression examines the linear relationships between one Dependent variable and two or more Independent variables.

With the best wishes

Mr Rezaei Ahvanooei.

Very useful answers.

Correlation asks if there is a relationship between two variables. One drawback is that a low correlation might result from a very strong but nonlinear relationship.

Regression is a tool for determining the relationship between two or more variables. I can have Y=b+mX1+e (univariate), or I can have Y=b+X1+X2+...+Xn+e (multiple). These are linear regression. I can have Y=b+m^X1 wherein I take the log of both sides to get log(Y)=log(b)+mLog(X1) which is once again linear, but is sometimes called curvilinear regression because the original relationship is curved. Finally, there is nonlinear regression where any relationship can be modeled. Regression also includes many tools to better understand how the relationship between dependent and independent variables does (or does not) work. Most popular is to analyze the residuals.

Correlation: is there a relationship? How strong is the relationship?

Regression: what is the relationship?

Correlation: pairwise

Regression: one or more independent variables.

Regression is by far the more powerful of the two methods.

I agree with Nihad mohammed, he has taken time to explain the key insight differences between linear regression analysis and correlation......

@Andem, I cannot find Nihad Mohammed as providing an answer to this question. Can you please clarify?

@Timothy A Ebert, one more thing I can add to your answer is that Correlation is misleading parameter in most cases. The key difference between correlation and regression is that correlation is a descriptive tool while regression is quantitative tool. thanks

Thanks to RG members for valuable input on the subject that is likely to bebefit researchers.