Book Image

Statistical Application Development with R and Python - Second Edition

Book Image

Statistical Application Development with R and Python - Second Edition

Overview of this book

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
Table of Contents (19 chapters)
Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

The essence of regression


The first linear regression model was built by sir Francis Galton in 1908. The word regression implies towards the center. The covariates, also known as independent variables, features, or regressors, have a regressive effect on the output, also called dependent or regressand variable. Since the covariates are allowed and assumed to affect the output in linear increments, we call the model the linear regression model. The linear regression models provide an answer for the correlation between the regressand and the regressors and, as such, do not really establish causation.

As will be seen later in the chapter, using data, we will be able to understand the mileage of a car as a linear function of the car-related dynamics. From a purely scientific point of view, the mileage should really depend on complicated formulas of the car's speed, road conditions, climate, and so on.

However, it will be seen that linear models work just fine for the problem despite not really...