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Building Statistical Models in Python

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
4.9 (11)
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Building Statistical Models in Python

Building Statistical Models in Python

4.9 (11)
By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
Table of Contents (22 chapters)
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1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

Simple Linear Regression

In previous chapters, we worked with distributions of single variables. Now we will discuss the relationships between variables. In this chapter and the next chapter, we will investigate the relationship between two or more variables using linear regression. In this chapter, we will discuss simple linear regression within the framework of Ordinary Least Squares (OLS) regression. Simple linear regression is a very useful tool for estimating continuous values from two linearly related variables. We will provide an overview of the intuitions and calculations behind regression errors. Next, we will provide an overview of the pertinent assumptions of linear regression. After that, we will analyze the output summary of OLS in statsmodels, and finally, we will address the scenarios of serial correlation and model validation. As highlighted, our main topics in this chapter follow this framework:

  • Simple linear regression using OLS
  • Coefficients of correlation...
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Building Statistical Models in Python
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