Correlation Vs. Regression: Differences And Similarities

By Indeed Editorial Team

Published 10 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Businesses often collect and analyse data full of financial indicators that can show the health of the country's economy or forecast future business trends within an industry. Sometimes two variables of this data may show an interdependent relationship upon one another, or one variable may affect one or multiple indicators at the same time. Understanding the differences and similarities between correlation and regression can help improve your statistics skills and help a company's employees make better business decisions.

In this article, we compare correlation vs. regression, discover key differences and similarities between correlation and regression and review two examples of these two statistical measures.

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Comparing correlation vs. regression

If you want to understand how some financial indicators may affect others and how these relations influence a company's decision-making process, you may benefit from comparing correlation vs. regression. These statistical measures are helpful in assessing simple relations between two variables and predicting outcomes based on indicators that are constantly shifting. For example, if you want to gauge the relationship between the summer season and the average price of gas, you can observe and record how prices rise or decrease during the following five summers. The interdependent relation amongst the variables may allow you to forecast the price of gas.

By using these concepts, you can also identify and highlight financial indicators that often affect many others, encourage adjustment in business budgets and generate policies to counterattack economic situations. For example, a chief financial officer (CFO) can monitor national interest rates to decide if it is better to suggest short- or long-term investments. With this information, they can also advise the board about hiring new employees and buying new equipment. Here, the interest rates become information with the ability to affect a company's liquidity and employment rates. To better understand these concepts, it's crucial to review their definitions, which include:

What is correlation?

Correlation refers to a statistical measure that describes a symbiotic relationship between two variables, which means one variable changes as the other changes as well. Although you can notice the relationship between both, a correlation does not explain causation, which means it does not provide you with the causes of that relation. It cannot help you forecast consequences either, but it is very important to identify a linear relationship between two factors, indicators or situations. You cannot establish a correlation between three or more elements, as this concept can only accept two variables.

Businesses may use correlation to demonstrate simple relations amongst data, which can be useful to restock inventory or produce a certain product. For example, a car manufacturer located in Maharashtra may benefit from correlated data, as it may notice a relationship between the demand for sports cars and its customers' average age. The company can assess this relationship to know how strong it is and then decide to produce more or fewer sports automobiles. Organisations are usually eager to discover new correlations amongst their data because they can represent unique business opportunities and market share.

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What is regression?

Regression refers to a linear relationship between one independent variable and one or more dependent variables. This means that you can identify a simple linear regression, a multiple linear regression and a complex non-linear regression. For instance, if a company issues some bonds to raise capital, it may increase its liquidity levels, which it can use to pay its operations expenses or purchase new equipment. Here, the bonds represent an independent variable that affects the company's liquidity, which is the dependent variable. If the company sold some assets to have more liquidity, this might not affect its bonds.

This statistical measure does not explain causes and consequences either, but it helps a company identify internal and external variables that affect its operations. Businesses can forecast sales, number of customers or market share within a year by monitoring some independent variables. For example, a real state organisation can predict an increase in its sales and a need for more employees because last year the inflation rate dropped by 3%. This relationship between inflation, the independent variable, and sales and the number of professionals needed, the dependent variables, cannot explain that it occurred because people have more money in saving accounts.

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Key differences and similarities between correlation and regression

Both statistical measures are helpful in pointing out simple relationships among data. They can also help gauge the strength of a relation between variables. Here is a list of differences and similarities between these two statistical measures:

  • External and internal indicators: Correlation can help a business establish a relationship between two internal variables, such as the number of non-direct sales and the price of the company's shares. While regression can be useful to establish a non-interdependent relation between one or more internal and dependant variables, such as direct sales, and one external variable, such as gross domestic product (GDP).

  • Curvilinear or complex relations: Correlation can only describe simple and linear relations between two variables, it cannot explain a more complex relationship. Regression can describe curvilinear associations, which are relations that depend on a pattern, such as a relationship between the inflation and the cost of raw materials and the cash available to borrow.

  • Methods to measure it: Correlation uses a formula to calculate the sample correlation coefficient, represented by the letter r, which measures the strength of the relationship between two interdependent variables. Regression also uses a formula to first determine the influence of one variable over another and then to describe the type of relationship, such as linear or inversely proportional.

  • Ways to express it: The sample correlation coefficient ranges from -1 to +1 and the closer it is to 0, the weaker the relationship is between the two variables. To represent a regression is better to use a scatter plot, which is a mathematical diagram where you can use dots to represent different numerical values.

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Examples of correlations and regression

Here are some examples to help you understand these two concepts:

Example of correlation

Below is an example of a correlation and how this may benefit a business to make decisions:

The chief financial officer (CFO) of Kerala Motors, a car engines manufacturer, wants to evaluate and report to the board of directors how they can adjust the products' prices according to the current inflation rate. The professional wants to do this to suggest flexible adjustments, which can enable the company to modify its prices monthly instead of yearly. To do this, the CFO looks for two variables with an interdependent relationship and with a clear influence over inflation rates. The professional starts to evaluate and monitor the raw materials price index (RMPI) and the national employment rates.

After studying them for seven months, the CFO calculates the correlation coefficient (r) and gets a value of 0.75, which means the RPMI increases when the national employment rate also increases. If the CFO got an r value of -0.75, the professional might conclude that the RPMI decreases when the national employment rate does the same. The CFO also noticed that the relationship between these variables is strong, so they can use it to predict month-to-moth a rise in the inflation rate. This may help Kerala Motors to adjust its prices and avoid any financial loss.

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Example of regression

Below is an example of regression and how this may be essential for a business to monitor its operations:

The CFO of Jabalpur Textiles, a company located in Madhya Pradesh, is noticing that the company's clients are increasing. They have noticed that the company is producing at its maximum capacity, which means it may need new machinery and more labour force. They want to know if this depends on the marketing campaigns or an external factor. Because the company's clients have offices across the country, the CFO starts studying the national gross domestic product (GDP) as their independent variable. They compare this to the number of customer orders and the number of overtime hours requested by employees.

After studying this situation for several months, the CFO prepares a scatter plot and notices that orders and overtime hours, their dependant variables, increase when the national GDP rises. With this information, the CFO may suggest to the board of directors to prepare a flexible plan to buy new equipment if the GDP increases and rent it to competitors when GDP decreases. They can also propose to hire more temporary employees and avoid overstaffing the company if the GDP decreases.

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