Regression analysis is a statistical modeling process that is used to find the extent of a relationship between variables. There are many types of regression analysis techniques like linear regression, logistic regression, etc.
Linear regression is a type of regression analysis technique that is used for statistical data analysis. It involves the determination of the extent to which a dependent variable is influenced by one or more independent variable. There are two main types of linear regression. They are simple and multiple linear regression. In simple linear regression, there are only two variables tested. One is dependent on the other, and the other is independent. In multiple linear regression, one variable is dependent on more than one other variable, while the other multiple variables are independent of the dependent variable.
In data science, the linear regression model is the oldest as well as the most widely used regression analysis technique. While learning about predictive analysis models, linear regression is usually learned as a beginner's module. Regression analysis is useful in discovering significant patterns in big data sets where comparison of data and relationships between two or more variables need to be analyzed on a large scale. Such techniques are important for data analytics due to two major reasons.
- They help differentiate the most important characteristic and least important characteristic between dependent and independent variable.
- They help identify the strength of each independent variable's relation to a dependent variable.
- They help in comparing the effects of variables measured on different scales.
Another aspect of linear regression, like most other traditional regression analysis models, is the use of assumptions about the data prior to analysis. These assumptions are needed in order for the data to fit into the linear regression model. Only then can any inference or results be obtained. This stems from the fact that linear regression developed and was put to use when there were no computers in existence and traditional statistical methods were the primary models available. These models had limited capability and could not be processed in large numbers and quick time simultaneously.
After the internet revolution and its rapid spread across the globe, data began to emerge from digital sources in mind-boggling numbers, complexity as well as in quick time. Traditional statistical models like linear regression analysis started facing problems in tackling larger data sets and began to give many false results that were the inherent problem of trying to fit data based on assumptions. Also, the linear regression models were not built to predict or analyze data sets that were off the scale produced by modern big data. When data was non-linear, the linear regression models simply could not cope due to their tendency to over-fit data and difficulties in interpreting parameters. And in the modern world data tends to be non-linear and needs to be analyzed to discover patterns of significance. Thus the linear regression may be helpful for small scale predictive analysis or interpolation but it is not suitable for large-scale predictive analysis, which is one of the important aspects of modern big data analytics.