Regression analysis is the starting point in data science. This is because regression models represent the most well-understood models in numerical simulation. Once we experience the workings of regression models, we will be able to understand all other machine learning algorithms. Regression models are easily interpretable as they are based on solid mathematical bases (such as matrix algebra for example). We will see in the following sections that linear regression allows us to derive a mathematical formula representative of the corresponding model. Perhaps this is why such techniques are extremely easy to understand.
Regression analysis is a statistical process done to study the relationship between a set of independent variables (explanatory variables) and the dependent variable (response variable). Through this technique, it will be possible to understand how the value of the response variable changes when the explanatory variable is varied.
Consider some data that is collected about a group of students, on: number of study hours per day, attendance at school, and scores on the final exam obtained. Through regression techniques, we can quantify the average increase in the final exam score when we add one more hour of study. Lower attendance in school (decreasing the student's experience) lowers the scores in the final exam.
A regression analysis can have two objectives:
- Explanatory analysis: To understand and weigh the effects of the independent variable on the dependent variable according to a particular theoretical model
- Predictive analysis: To locate a linear combination of the independent variable to predict the value assumed by the dependent variable optimally
In this chapter, we will be introduced to the basic concepts of regression analysis, and then we'll take a tour of the different types of statistical processes. In addition to this, we will also introduce the R language and cover the basics of the R programming environment. Finally we will explore the essential tools that R provides for understanding the amazing world of regression.
We will cover the following topics:
- The origin of regression
- Types of algorithms
- How to quickly set up R for data science
- R packages used throughout the book
At the end of this chapter, we will provide you with a working environment that is able to run all the examples contained in the following chapters. You will also get a clear idea about why regression analysis is not just an underrated technique taken from statistics, but a powerful and effective data science algorithm.
