Correlation vs. Regression: Key Differences and Similarities

We’ve all heard “correlation doesn’t imply causation,” but what does it really mean?

It all comes down to correlation vs. regression, statistical analysis measurements used to find connections between two variables, measure the connections, and make predictions. Statistical analysis software empowers businesses to conduct these complex processes through data importation, preparation, and statistical modeling.

Investigating the relationship between two variables requires knowing the differences and similarities between correlation and regression. It’s common to be confused between these two terms, as correlation can often drive into regression. However, there is a key difference.

Essentially, you must know when to use correlation vs regression. Use correlation to summarize the strength and degree of the relationship between two or more numeric variables. Use regression when you’re looking to predict, optimize, or explain a number response between the variables (how x influences y).

Correlation vs. regression: Overview

Measuring correlation and regression is common for businesses, but it's also seen in our daily lives. For instance, have you ever seen someone driving an expensive car and automatically thought that the driver must be financially successful? Or how about thinking that the further you run on your morning workout, the more weight you’ll lose?

Both are examples of real-life correlation and regression, as you see one variable (a fancy car or a long workout) and then check if there is any direct relation to another variable (being wealthy or losing weight). 

Regardless of what you’re using correlation and regression to see, utilizing a business intelligence platform is the best way to analyze the data you’re looking at in a way that is easy to pinpoint which actionable insights to take. Mining your data with business intelligence software allows for a simple examination of big data, real-time data, and unstructured data and determines areas for improvement and other notable trends.

If you aren't looking for business intelligence or analytics platforms but are still hoping to calculate correlation and regression, you're able to find both using various Excel formulas. Remember that a BI platform is your best bet for increased efficiency and accuracy. 

What is correlation?

To simply define correlation, think of it as the combination of the words “co” meaning together, and “relation” meaning a connection between two quantities.

In this sense, correlation is when a change follows a change to one variable in another, whether direct or indirect. Variables are believed to be “uncorrelated” when a change in one does not affect the other. In short, it measures the relationship between two variables.

For example, let’s say our two variables are x and y. The type of correlation between these two variables can be considered positive or negative. A positive change would be when two variables move in the same direction, meaning an increase in one variable will cause an increase in another. So, if an increase in x increases y, it’s positively correlated.

An example of this would be demand and price. An increase in demand causes an increase in price. The price increases because there are more consumers who want it and are willing to pay more for it. 

Suppose two variables are moving in opposite directions, like when an increase in one variable results in a decrease in another. This is known as a negative correlation. An example of a negative correlation is the price and demand for two products because an increase in price (x) results in a decrease in demand (y).

Knowing how two variables are correlated allows for predicting trends in the future, as you’ll be able to understand the relationship between the variables — or if there's no relationship at all.

Correlation analysis

The main purpose of correlation, through the lens of correlation analysis, is to allow experimenters to know the association or the absence of a relationship between two variables. When these variables are correlated, you’ll be able to measure the strength of their association.

Overall, correlation analysis aims to find the numerical value that shows the relationship between the two variables and how they move together.

One key benefit of correlation is that it is a more concise and clear summary of the relationship between the two variables than you’ll find with regression.

Correlation examples

A correlation chart, also known as a scatter diagram, makes it easier to see the correlation between two variables visually. Data in a correlation chart is represented by a single point. In the chart above, you can observe that correlation plots various points of single data.

Let's think of correlation as real-life scenarios. In addition to the price and demand example above, from a financial lens, the longer you invest, the more compound interest you will earn. Or, Hiring more salespeople results in higher revenue due to the company making more sales.

Now let's look at correlation from a marketing standpoint to see the strength of a relationship between the two variables. For instance, it could be in your company's best interest to see if there is a predictable relationship between the sale of a product and factors like weather, advertising, and consumer income.  

What is regression?

On the other hand, regression is how one variable affects another or changes in a variable that trigger changes in another, essentially cause and effect. It implies that the outcome is dependent on one or more variables.

For instance, while correlation can be defined as the relationship between two variables, regression is how they affect each other. An example of this would be how an increase in rainfall would cause various crops to grow, just like a drought would cause crops to wither or not grow.

When the dependent variable increases while the independent variable decreases, or vice versa, it's called a negative regression. This contrasts with a positive regression, where both dependent and independent variables increase together. 

Regression analysis

Regression analysis helps to determine the functional relationship between two variables (x and y) so that you can estimate the unknown variable and make future projections on events and goals.

The main objective of regression analysis is to estimate the values of a random variable (z) based on the values of your known (or fixed) variables (x and y). Linear regression analysis is considered to be the best-fitting line through the data points. 

The main advantage of using regression within your analysis is that it provides a detailed look at your data (more detailed than correlation alone) and includes an equation that can be used to predict and optimize your data in the future.

When the line is drawn using regression, we can see two pieces of information:

Regression examples

When it comes to using regression, we at G2 utilize regression to predict certain trends, like how our traffic is expected to grow over the coming months.

One person who uses regression is an SEO and Data Analyst. Visualizing data, analyzing it, identifying trends, and predicting what the data could look like in the future is a big part of their job. Many teams rely on their work to set team goals and understand how our traffic could look in the future.

They also use the predictions from regression-based models to set goals for important company metrics, like keyword acquisition. Since the predictions are based on historical data, this gives the company insights into how it is currently trending compared to past growth trends.

Difference between correlation and regression

There are some key differences between correlation and regression that are important in understanding the two. 

• Interchangeable factors: Regression establishes how x causes y to change, and the results will change if x and y are swapped. With correlation, x and y are variables that can be interchanged and get the same result.
• Single data point vs. equation: Correlation is a single statistic, or data point, whereas regression is the entire equation with all of the data points that are represented with a line.
• Relationship vs. effect: Correlation shows the relationship between the two variables, while regression allows us to see how one affects the other.
• Cause and effect: The data shown with regression establishes a cause and effect. When one changes, so does the other, and not always in the same direction. With correlation, the variables move together.

Similarities between correlation and regression

In addition to differences, there are some key similarities between correlation and regression that can help you to better understand your data.

• Both work to quantify the direction and strength of the relationship between two numeric variables.
• Any time the correlation is negative, the regression slope (line within the graph) will also be negative.
• Any time the correlation is positive, the regression slope (line within the graph) will be positive.

Frequently asked questions about correlation and regression

Q. What are regression and correlation in statistics?

Correlation and regression are techniques used to analyze the relationship between two quantitative variables. While correlation measures the strength of a linear relationship between two variables, regression in statistics measures how those variables affect each other using an equation.

Q. What is one key difference between regression and correlation?

Correlation determines the connection or relationship between two numerical variables. Regression focuses on how that relationship will impact each of the variables over time.  

Q. Should I use correlation or regression?

Use correlation to know the degree of a relationship between two variables. But if you want to analyze the effect of how an independent variable is numerically associated with the dependent variable, use regression.

Q. Can you do correlation and regression together?

Yes, correlation and regression analysis can be conducted together to measure a data set and understand the relationship between variables. 

It's more than cause and effect.

Even though they’re studied together, it’s clear that there are obvious differences and similarities between correlation and regression.

When you’re looking to build a model, an equation, or predict a key response, use regression. If you’re looking to summarize the direction and strength of a relationship quickly, correlation is your best bet.

To further conceptualize your data, use data visualization software and track your business metrics and KPIs in real time.

This article was originally published in 2020. It has been updated with new information.