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5 Uses of Regression Analysis in Business

Source: New Gen Apps

What is Regression Analysis?

Regression analysis is a statistical technique used to find the relations between two or more

variables. In regression analysis one variable is independent and its impact on the other

dependent variables is measured. When there is only one dependent and independent variable we

call is simple regression. On the other hand, when there are many independent variables

influencing one dependent variable we call it multiple regression.

Let’s understand it with a simple example. Suppose you have a lemonade business. A

simple linear regression could mean you finding a relationship between the revenue and

temperature, with revenue as the dependent variable. In case of multiple variable regression, you

can find the relationship between temperature, pricing and number of workers to the revenue.

Thus, regression analysis can analyze the impact of varied factors on business sales and profits.

5 Uses of Regression Analysis in Business

1. Predictive Analytics:

Predictive analytics i.e. forecasting future opportunities and risks is the most prominent

application of regression analysis in business. Demand analysis, for instance, predicts the

number of items which a consumer will probably purchase. However, demand is not the only

dependent variable when it comes to business. Regression analysis can go far beyond forecasting

impact on direct revenue. For example, we can forecast the number of shoppers who will pass in

front of a particular billboard and use that data to estimate the maximum to bid for an

advertisement. Insurance companies heavily rely on regression analysis to estimate the credit

standing of policyholders and a possible number of claims in a given time period.

2. Operation Efficiency:

Regression models can also be used to optimize business processes. A factory manager, for

example, can create a statistical model to understand the impact of oven temperature on the shelf

life of the cookies baked in those ovens. In a call center, we can analyze the relationship between

wait times of callers and number of complaints. Data-driven decision making eliminates

guesswork, hypothesis and corporate politics from decision making. This improves the business

performance by highlighting the areas that have the maximum impact on the operational

efficiency and revenues.

3. Supporting Decisions:

Businesses today are overloaded with data on finances, operations and customer purchases.

Increasingly, executives are now leaning on data analytics to make informed business decisions

thus eliminating the intuition and gut feel. Regression analysis can bring a scientific angle to the

management of any businesses. By reducing the tremendous amount of raw data into actionable

information, regression analysis leads the way to smarter and more accurate decisions. This does

not mean that regression analysis is an end to managers creative thinking. This technique acts as

a perfect tool to test a hypothesis before diving into execution.

4. Correcting Errors:

Regression is not only great for lending empirical support to management decisions but also for

identifying errors in judgment. For example, a retail store manager may believe that extending

shopping hours will greatly increase sales. Regression analysis, however, may indicate that the

increase in revenue might not be sufficient to support the rise in operating expenses due to longer

working hours (such as additional employee labor charges). Hence, regression analysis can

provide quantitative support for decisions and prevent mistakes due to manager's intuitions.

5. New Insights:

Over time businesses have gathered a large volume of unorganized data that has the potential to

yield valuable insights. However, this data is useless without proper analysis. Regression

analysis techniques can find a relationship between different variables by uncovering patterns

that were previously unnoticed. For example, analysis of data from point of sales systems and

purchase accounts may highlight market patterns like increase in demand on certain days of the

week or at certain times of the year. You can maintain optimal stock and personnel before a

spike in demand arises by acknowledging these insights.