# Correlation vs. Regression: Know the Difference

By Shumaila Saeed || Published on January 1, 2024

**Correlation measures the strength and direction of a relationship between two variables, while regression predicts the value of a dependent variable based on the value of an independent variable.**

## Key Differences

Correlation is a statistical measure that expresses the extent to which two variables change together. It does not imply causation but merely indicates how closely the variables move in relation to each other. Regression, in contrast, is used to fit a line through the data points that best expresses the relationship between those variables, typically used to make predictions or understand the relationship between the independent (predictor) and dependent (outcome) variable.

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Jan 01, 2024

The primary purpose of correlation is to quantify the degree of association between two variables, providing a value between -1 and 1 to describe this. This value, known as the correlation coefficient, doesn't provide information on the individual data points. Regression, however, specifically aims to establish a formula to predict or explain one variable based on another. It involves finding the best-fitting line or curve for the data points.

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Jan 01, 2024

In correlation analysis, the variables are treated equally; the focus is on the relationship, not on predicting one from the other. It's symmetrical, meaning the correlation between X and Y is the same as the correlation between Y and X. Regression analysis is asymmetrical. It treats one variable as independent and the other as dependent, aiming to predict the latter from the former.

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Jan 01, 2024

A key aspect of correlation is that it doesn’t assume or imply any cause-and-effect relationship. It only assesses how variables are related. In regression, there is often an implied direction (though not necessarily causation), as it attempts to show how a dependent variable changes when an independent variable changes.

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Jan 01, 2024

Correlation is often the first step in data analysis, used to identify relationships between variables, after which regression is used to explore the nature of the relationship in more detail, particularly for the purposes of prediction or causal inference.

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Jan 01, 2024

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## Comparison Chart

### Purpose

Measures strength and direction of a relationship

Predicts or explains one variable based on another

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### Nature

Descriptive, indicating how variables move together

Predictive, fitting a line or curve for prediction

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### Symmetry

Symmetrical, treating variables equally

Asymmetrical, distinguishing independent and dependent variables

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### Value Range

Correlation coefficient ranges from -1 to 1

Involves coefficients for line or curve equation

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## Correlation and Regression Definitions

#### Correlation

A measure that indicates the extent to which two or more variables fluctuate together.

The correlation between economic growth and unemployment rates is often analyzed.

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#### Regression

The process of predicting a dependent variable based on the values of independent variables.

Using regression, they predicted annual revenue based on advertising spend.

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#### Correlation

A method to quantify the strength of a relationship between two variables.

Researchers found a strong correlation between diet and health outcomes.

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#### Regression

A technique to determine the best-fitting line through a set of data points.

Linear regression was used to understand how age affects blood pressure.

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#### Correlation

A statistical index ranging from -1 to 1, showing the linear relationship between variables.

A correlation of -1 indicates a perfect negative relationship between two variables.

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#### Regression

A statistical method for estimating the relationships among variables.

Regression analysis showed that temperature is a significant predictor of ice cream sales.

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#### Correlation

A relationship or connection between two things based on co-occurrence or pattern of change

A correlation between drug abuse and crime.

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#### Regression

A tool for modeling the relationship between a scalar response and one or more explanatory variables.

Multiple regression analysis was conducted to explore the factors affecting house prices.

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#### Correlation

(Statistics) The tendency for two values or variables to change together, in either the same or opposite way

As cigarette smoking increases, so does the incidence of lung cancer, indicating a positive correlation.

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#### Regression

The analysis of the impact of a unit change in the independent variable on the dependent variable.

The regression model indicated that for every hour of study, the exam score increases by 5 points.

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#### Regression

The process or an instance of regressing, as to a less perfect or less developed state.

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#### Correlation

A reciprocal, parallel or complementary relationship between two or more comparable objects.

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#### Regression

(Psychology) In psychoanalytic theory, reversion to an earlier or less mature stage of psychological development.

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#### Correlation

(statistics) One of the several measures of the linear statistical relationship between two random variables, indicating both the strength and direction of the relationship.

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#### Correlation

(algebra) An isomorphism from a projective space to the dual of a projective space, often to the dual of itself.

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#### Regression

(Statistics) A technique for predicting the value of a dependent variable as a function of one or more independent variables in the presence of random error.

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#### Correlation

Reciprocal relation; corresponding similarity or parallelism of relation or law; capacity of being converted into, or of giving place to, one another, under certain conditions; as, the correlation of forces, or of zymotic diseases.

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#### Regression

(Geology) A relative fall in sea level resulting in deposition of terrestrial strata over marine strata.

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#### Correlation

A statistic representing how closely two variables co-vary; it can vary from -1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation);

What is the correlation between those two variables?

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#### Correlation

A statistical relation between two or more variables such that systematic changes in the value of one variable are accompanied by systematic changes in the other

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#### Correlation

A statistical measure of how two variables move in relation to each other.

There is a high correlation between smoking and lung cancer incidence.

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#### Regression

(psychotherapy) A psychotherapeutic method whereby healing is facilitated by inducing the patient to act out behaviour typical of an earlier developmental stage.

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#### Correlation

The degree to which two variables are linearly related.

The correlation between hours studied and exam scores was significant.

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#### Regression

(statistics) An analytic method to measure the association of one or more independent variables with a dependent variable.

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#### Regression

(statistics) An equation using specified and associated data for two or more variables such that one variable can be estimated from the remaining variable(s). Category:en:Functions

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#### Regression

(programming) The reappearance of a bug in a piece of software that had previously been fixed.

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#### Regression

(medicine) The diminishing of a cellular mass like a tumor, or of an organ size.

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#### Regression

(exercise) The making an exercise less straining to perform by manipulating the details of its performance like loaded weight, range of motion, angle, speed.

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#### Regression

The act of passing back or returning; retrogression; retrogradation.

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#### Regression

(psychiatry) a defense mechanism in which you flee from reality by assuming a more infantile state

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#### Regression

The relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)

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## Repeatedly Asked Queries

#### What is the main use of regression analysis?

To predict or explain one variable based on another.

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#### What does a correlation coefficient of 0 mean?

It indicates no linear relationship between the variables.

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#### Can regression analysis determine cause and effect?

It can suggest but not conclusively establish causation.

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#### Can regression be used for prediction?

Yes, it’s commonly used for making predictions.

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#### What type of data is needed for correlation analysis?

Two or more variables to observe their relationship.

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#### Is regression analysis symmetric?

No, it distinguishes between independent and dependent variables.

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#### What does correlation measure?

Measures the strength and direction of the relationship between two variables.

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#### What does a high positive correlation coefficient indicate?

A strong positive linear relationship between variables.

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#### How are correlation and regression related?

Correlation is often a preliminary step before regression analysis.

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#### Can you use regression with multiple independent variables?

Yes, in multiple regression analysis.

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#### Can correlation coefficients exceed 1 or -1?

No, they range only between -1 and 1.

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#### What's an example of a non-linear relationship?

A relationship that can't be represented with a straight line.

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#### How is correlation useful in real-world applications?

For identifying relationships between variables in various fields.

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#### Can regression analysis handle categorical variables?

Yes, through techniques like dummy coding.

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#### What does a negative correlation signify?

As one variable increases, the other decreases.

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#### Is linear regression the only type of regression analysis?

No, there are multiple types, including logistic regression.

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#### How is correlation different from covariance?

Correlation is standardized, while covariance is not.

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#### Does regression require a linear relationship?

Linear regression does, but there are non-linear regression models too.

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#### What is the significance of the regression line?

It represents the best fit line that predicts the dependent variable.

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Jan 01, 2024

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About Author

Written by

Shumaila SaeedShumaila Saeed, an expert content creator with 6 years of experience, specializes in distilling complex topics into easily digestible comparisons, shining a light on the nuances that both inform and educate readers with clarity and accuracy.