When investigating the relationship between two or more numeric variables, it is important to know the difference between correlation and regression. The similarities/differences and advantages/disadvantages of these tools are discussed here along with examples of each.

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Correlation quantifies the direction and strength of the relationship between two numeric variables, X and Y, and always lies between -1.0 and 1.0. Simple linear regression relates X to Y through an equation of the form Y = a + bX.


Key similarities

Both quantify the direction and strength of the relationship between two numeric variables.When the correlation (r) is negative, the regression slope (b) will be negative.When the correlation is positive, the regression slope will be positive.

Key differences

Regression attempts to establish how X causes Y to change and the results of the analysis will change if X and Y are swapped. With correlation, the X and Y variables are interchangeable.Regression assumes X is fixed with no error, such as a dose amount or temperature setting. With correlation, X and Y are typically both random variables*, such as height and weight or blood pressure and heart rate.Correlation is a single statistic, whereas regression produces an entire equation.

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*The X variable can be fixed with correlation, but confidence intervals and statistical tests are no longer appropriate. Typically, regression is used when X is fixed.

Learn more about correlation vs regression analysis with this video by365 Data Science

Key advantage of correlation

Correlation is a more concise (single value) summary of the relationship between two variables than regression. In result, many pairwise correlations can be viewed together at the same time in one table.

Key advantage of regression

Regression provides a more detailed analysis which includes an equation which can be used for prediction and/or optimization.

Correlation Example

As an example, let’s go through the Prism tutorial on correlation matrix which contains an automotive dataset with Cost in USD, MPG, Horsepower, and Weight in Pounds as the variables. Instead of just looking at the correlation between one X and one Y, we can generate all pairwise correlations using Prism’s correlation matrix. If you don’t have access to Prism, download the free 30 day trial here. These are the steps in Prism:

Choose Start with sample data to follow a tutorial and select Correlation matrix.Click Create.Click Analyze.Select Multiple variable analyses > Correlation matrix.Click OK twice.On the left side panel, double click on the graph titled Pearson r: Correlation of Data 1.


The Prism correlation matrix displays all the pairwise correlations for this set of variables.

The red boxes represent variables that have a negative relationship.The blue boxes represent variables that have a positive relationshipThe darker the box, the closer the correlation is to negative or positive 1.Ignore the dark blue diagonal boxes since they will always have a correlation of 1.00.

Key findings:

Horsepower and MPG have a strong negative relationship (r = -0.74), higher horsepower cars have lower MPG.Horsepower and cost have a strong positive relationship (r = 0.88), higher horsepower cars cost more.

Note that the matrix is symmetric. For example, the correlation between “weight in pounds” and “cost in USD” in the lower left corner (0.52) is the same as the correlation between “cost in USD” and “weight in pounds” in the upper right corner (0.52). This reinforces the fact that X and Y are interchangeable with regard to correlation. The correlations along the diagonal will always be 1.00 and a variable is always perfectly correlated with itself.

When interpreting correlations, you should be aware of the four possible explanations for a strong correlation:

Changes in the X variable causes a change the value of the Y variable.Changes in the Y variable causes a change the value of the X variable.Changes in another variable influence both X and Y.X and Y don’t really correlate at all, and you just happened to observe such a strong correlation by chance. The P value quantifies the likelihood that this could occur.Regression Example

The strength of UV rays varies by latitude. The higher the latitude, the less exposure to the sun, which corresponds to a lower skin cancer risk. So where you live can have an impact on your skin cancer risk.Two variables, cancer mortality rate and latitude, were entered into Prism’s XY table. The Prism graph (right) shows the relationship between skin cancer mortality rate (Y) and latitude at the center of a state (X). It makes sense to compute the correlation between these variables, but taking it a step further, let’s perform a regression analysis and get a predictive equation.


The relationship between X and Y is summarized by the fitted regression line on the graph with equation: mortality rate = 389.2 - 5.98*latitude. Based on the slope of -5.98, each 1 degree increase in latitude decreases deaths due to skin cancer by approximately 6 per 10 million people.

Since regression analysis produces an equation, unlike correlation, it can be used for prediction. For example, a city at latitude 40 would be expected to have 389.2 - 5.98*40 = 150 deaths per 10 million due to skin cancer each year.Regression also allows for the interpretation of the model coefficients:

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Summary and Additional Information

In summary, correlation and regression have many similarities and some important differences. Regression is primarily used to build models/equations to predict a key response, Y, from a set of predictor (X) variables. Correlation is primarily used to quickly and concisely summarize the direction and strength of the relationships between a set of 2 or more numeric variables.

The table below summarizes the key similarities and differences between correlation and regression.




When to use

For a quick and simple summary of the direction and strength of pairwise relationships between two or more numeric variables.

See more: How Many Calories In Lays Chips Big Bag, How Many Calories Are In A Bag Of Baked Lays

To predict, optimize, or explain a numeric response Y from X, a numeric variable thought to influence Y.

Quantifies direction of relationship



Quantifies strength of relationship



X and Y interchangeable



Y Random



X Random



Prediction and Optimization






Extension to curvilinear fits



Cause and effect


Attempts to establish

Learn more about how to choose between regression and correlation on Prism Academy