Regression analysis is a set of statistical methods for evaluating relationships between variables. It can be used to assess the degree of relationship between variables and to model future dependencies. In fact, regression methods show how changes in the “independent variables” can be used to capture the change in the “dependent variable”.
The Independent variable is called a predictor, while the dependent one is called predictant (a characteristic that is observed to change). In case of business, predictant value could be sales changes, risk fluctuation, price changes, market performance, and so on.
Regression analysis includes several models. The most common ones are linear, multilinear (or multiple linear), and nonlinear. Also, it has many approaches. Get a clear explanation of how regression analysis works here.
Let us consider in more detail, how it works.
Regression analysis approaches
Regression analysis has two approaches:
- Predictive Analytics
- Machine Learning
Predictive Analytics:
This approach has a very specific purpose, it uses the historic data in order to predict the future outcomes. It can also be called as data science. E.g., Predictive analytics can provide an extra sense of confidence in regards to a question like “How much monthly sales can we do”.
Machine learning can be an additive tool for the practice of predictive analytics. Using ML as extension, predictive analytics can:
- Helps answer or solve complex problems with ease.
- With answers to complex problems, it opens up possibilities or approaches to new problems.
- It not only just answers real-time questions that persist through time but also has ever variating data.
Here is an example how machine learning expands predictive analytics. By using ML with predictive analysis, it will expand on conducting feasibility analysis of a marketing campaign for a business. It will explain the changes or percentage of success ratio for a particular marketing campaign.
Machine Learning:
This one is different from the predictive analytics approach. It is the best tool to conduct statistical analysis. It is self-learning; it can optimize or alter its parameters of it model, just according to the data available.
It is used by many big companies like Amazon, Google, Microsoft and many more for many different apps. It is safe to say that machine learning has nothing to do with speaking about some audience. It is like physics or calculus, the best tool to be used.
Regression Analysis Business Applications
Here are some of the best business applications that regression analysis brings:
Forecasting indicators
This model can be used for trend detection and forecasting. Let’s say the company’s sales have been growing for two years. By performing a linear analysis of the monthly sales data, the company could forecast sales in the coming months.
Evaluating the effectiveness of marketing
Linear regression can also be used to measure the effectiveness of marketing, advertising campaigns, and pricing. In order for XYZ to assess the quality return on funds spent on marketing a particular brand, it is enough to plot a linear regression graph and see how costs are related to profit.
The beauty of linear regression is that it allows you to capture the individual impacts of each marketing campaign, as well as control the factors that can affect sales.
In real life scenarios, there are usually several advertising campaigns that run at the same time period. Suppose two campaigns are launched on TV and radio in parallel. The constructed model can capture both isolated and combined effects of the simultaneous display of this advertisement.
Risk assessment
A linear regression model works well for calculating risks in finance or insurance. For example, a car insurance company can construct a linear regression to compile a table of insurance premiums using the ratio of predicted claims to claimed insured value. The main factors in this situation are vehicle characteristics, driver data or demographic information. The results of this analysis will help you make important business decisions.
Finding important factors
In the lending industry, a finance company is interested in minimizing risks. Therefore, it is important for her to understand the five main factors causing the insolvency of the client. Based on the results of the regression analysis, the company could identify these factors and determine the EMI options (Equated Monthly Installment – a fixed payment made by the borrower to the lender within a specified period) in order to minimize default among dubious clients.
Asset pricing
Another linear regression model finds its application in asset pricing. The Long-Term Assets Pricing Model describes the relationship between the expected return and the risk of investing in a security. This helps investors assess the feasibility of an investment and the return on their portfolio.
Conclusion
Businesses can make use of regression analysis in a lot of ways for their own growth. With lots of models and complexities, it’s better to hire data scientists and machine learning gurus to get on road of prosperity. This is a very interesting and important thing, which is why this industry is on such a boom right now!