Regression Analysis – Data Management & predictions based on data

If there’s one term that has been highly talked about in the world of engineering, it’d be Regression analysis. Many businesses across the world have opted for this, and it’s worked wonders for them. So, what is Regression analysis and why is it important in current times?

Regression analysis is a machine learning tool that’s used to analyze data. Extremely powerful and insightful, it predicts happenings between a dependent (target) and independent variables. For instance, it can be used to predict the relationship between reckless driving and the total number of road accidents caused by the driver or the impact on sales with respect to advertising spends. It is one of the most common models when it comes to machine learning. As it estimates a numerical value, it differs from classification models. The primary use of this analysis is forecasting, time series modeling, and understanding ‘the cause and effect’ relationship between variables.

Why is Regression analysis important?

This analysis has a wide range of real-life applications. It is used for:

1. Financial forecasting
2. Testing automobiles
3. Sales and Promotions forecasting
4. Time series forecasting

Besides telling a significant relationship between two or more variables, regression analysis gives details about that particular relationship. It can tell you the impact that multiple variables will have on a dependent variable. For example: If you change the value of one variable (price), regression analysis should tell you what effect this will have on the dependent variable (sales).

With the help of data modeling, regression analysis is the best way of solving regression problems in machine learning. Businesses can test the effects of variables as measured on different scales.

So, opt for regression analysis in your business, unlock the potential of your data and make the most out of the insight.