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Building Statistical Models in Python

You're reading from  Building Statistical Models in Python

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781804614280
Pages 420 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
Huy Hoang Nguyen Huy Hoang Nguyen
Profile icon Huy Hoang Nguyen
Paul N Adams Paul N Adams
Profile icon Paul N Adams
Stuart J Miller Stuart J Miller
Profile icon Stuart J Miller
View More author details

Table of Contents (22) Chapters

Preface 1. Part 1:Introduction to Statistics
2. Chapter 1: Sampling and Generalization 3. Chapter 2: Distributions of Data 4. Chapter 3: Hypothesis Testing 5. Chapter 4: Parametric Tests 6. Chapter 5: Non-Parametric Tests 7. Part 2:Regression Models
8. Chapter 6: Simple Linear Regression 9. Chapter 7: Multiple Linear Regression 10. Part 3:Classification Models
11. Chapter 8: Discrete Models 12. Chapter 9: Discriminant Analysis 13. Part 4:Time Series Models
14. Chapter 10: Introduction to Time Series 15. Chapter 11: ARIMA Models 16. Chapter 12: Multivariate Time Series 17. Part 5:Survival Analysis
18. Chapter 13: Time-to-Event Variables – An Introduction 19. Chapter 14: Survival Models 20. Index 21. Other Books You May Enjoy

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
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