What is Regression Analysis?

Regression analysis in business is a statistical method used to find the relations between two or more independent and dependent variables. One variable is independent and its impact on the other dependent variables is measured. Broadly speaking, there are more than 10 types of regression models. When there is only one dependent and independent variable, or predictor 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.

Linear regression model is a linear approach to modeling the relationship between a scalar response and one or many explanatory variables. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. 

Nonlinear regression is a form of regression analysis where data fits a model and is then expressed as a mathematical function. Nonlinear regression is computed by finding the difference between the fitted nonlinear function and every Y point of data in the set.

Let’s understand it with a simple example. Suppose you have a lemonade business. A simple linear regression real life example could mean you finding a relationship between the revenue and temperature, with a sample size for 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.

There is also time series regression. Time series regression is a statistical method for predicting a future response based on the response history. Time series regression helps in the understanding of and predicting the behavior of dynamic systems from experimental or observational data.

Here some of its uses in business:

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5 Uses of Regression Analysis in Business:

Applications 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. RA 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. Data Science understanding is key for predictive analytics.

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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 that have statistical significance, thus eliminating the intuition and gut feel. RA 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 RA 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. RA, 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, this 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. RA 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.

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