Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

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)
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

Discrete Models

In the previous two chapters, we discussed models for predicting a continuous response variable. In this chapter, we will begin discussing models for predicting discrete response variables. We will start by discussing the probit and logit models for predicting binary outcome variables (categorical variables with two levels). Then, we will extend this idea to predicting categorical variables with multiple levels. Finally, we will look at predicting count variables, which are like categorical variables but only take values of integers and have an infinite number of levels.

In this chapter, we’re going to cover the following main topics:

  • Probit and logit models
  • Multinomial logit model
  • Poisson model
  • The negative binomial regression model