Regression models are the first step into Machine Learning.

To understand linear regression, we must first understand regression with a simple example. Let’s say you have a construction business. A simple linear regression could help you find a relationship between revenue and temperature, with revenue as the dependent variable. If there are multiple variables, then you can use logistic regression, which helps you find the relationship between temperature, pricing and number of workers affecting the revenue. Thus, regression analysis can analyze the impact of various factors on sales and profit.
Implementing regression models in business, is valuable and today’s data volumes allows you to make use of it in multiple forms: 

1. Predictive Analytics:

This type of analysis uses historical data, finds patterns, looks out for trends and uses that information to build predictions about future trends.

Regression analysis can go far beyond forecasting impact on immediate revenue. For example, you can forecast the number of customers who will purchase a service and use that data to estimate the amount of workforce needed to run that service. Insurance companies make use of regression analysis to estimate credit health of policy holders and a possible number of claims in a given time period.

Predictive analytics helps companies:

  • Reduce Costs
  • Reduce the amount of tools needed
  • Provide faster results
  • Improve operational efficiency
  • Help in fraud detection
  • Risk management
  • Optimize marketing campaigns

2. Operational Efficiency:

Regression models can also help optimize business processes. A factory director, for example, can build a regression model to understand the impact of the premises temperature on the overall productivity of all employees. In an ER hospital, we can analyze the relationship between the wait times of patients and the outcomes. 

3. Decision making:

Because the loads of data gathered on finances, operations and purchases, companies are now learning how to make use of data analytics to make data-driven decisions and not intuitive decisions. Linear and logistic regression, provides a more accurate analysis which can then be used to test hypotheses of situations prior to sending it to production.

4. Errors:

Regression analysis is not only valuable in providing insights for decision making, but also to identify errors in judgement. For example, executives managing a store may think that adding after hours shopping will increase profit. Regression analysis, however, analyzes all the variables revolving around this action and may conclude that to support the increase in operating expenses due to longer working hours (such as additional employee labor charges) will decrease profit significantly. Regression analysis provides quantitative support for decisions and prevents mistakes, product of intuitiveness.

5. New Insights:

Over time businesses have gathered a large volume of cluttered data that can provide invaluable amounts of new insights. Unfortunately, this data is of no use without the appropriate analysis. Regression analysis can find a relationship between several variables by uncovering patterns that were not taken into account. “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.” -Anurag

Data-driven decision eliminates the need to guess, and shields companies from making gut decisions. This greatly improves business performance by focusing on the areas with the most impact on the operationally and in revenue.