Statistics is used in business for: appraisal of value, consumer surveys, hiring decisions, insurance, manufacturing, online business, real estate investing, rental housing, sales, and stock markets. Data analysis, regression, forecasting, hypothesis testing, and more are used in these fields[1].
One of the biggest challenges of modern technology is avalanche of data or data overload. With the aid of Statistics and tools it offers, business leaders and managers can make decisions smartly, quickly, efficiently, and effectively. Of course, business issues can range from simple challenge such as determining how many people patronize a particular product brand in a supermarket during summer sales to more complex and complicated issue such as determining the potential acceptability and profitability of a new product to be launched into market already dominated by a brand that provides same service to consumers.
There are many statistical tools like Mean, Standard Deviation, Regression, Correlation, etc available to business managers to aid in making meaning out of tons of data available in today’s business world.
Correlation and Regression stands out as both tools not only compliment each other but the two tools are very useful. Results of Correlation analysis are useful in ‘forecast sales, product development, predicting future trends, optimize operations, better customer experience strategies[2]’ especially in manufacturing, production, customer services and hospitability industry. Regression analysis on the other hand is ‘used to evaluate the relationship between two or more variables’[3].
Correlation analysis tells us about the relationship between two or more variables especially the direction of the relationship (positive or negative) and strength of the relationship (week or strong). For instance, consider a business portfolio with two products (A and B) in the market. If the performance of both products A and B are in the same direction, it means the correlation coefficient between the products A and B is positive and if the performance of A and B are in opposite direction, it means there is negative correlation coefficient. In terms of strength, a correlation coefficient is much stronger as it tends to +1 or -1 and weak. Correlation coefficient is always between -1 and +1.
It is important to mention that Correlation does not imply causation between variables as each variable is independent. In addition, Correlation cannot different between dependent and independent variables and not also useful for forecasting.
Enter Regression analysis which cover up adequately for gaps listed with use of Correlation analysis. Regression analysis shows the relationship between two or more variables, determine the cause-effect factors between variables and can be used to effectively forecast or predict performance of market variables.
Regression is also deployed in making strategic business decisions, understand why a product failed to perform to expectation and learn to correct in future analysis. For instance, in pharmaceutical industry, regression is used to analyses the quantitative stability data for the retest period or estimation of shelf life, managing risk of credit card default by predicting expected customer behavior, credit balance, etc thereby supporting the company with information to minimize credit card default among risky customers[4].
[1] How Is Statistics Used In Business? (10 Real Life Examples) – JDM Educational
[3] Why Regression Analysis Is The Backbone For Enterprises (analyticsindiamag.com)
