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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

Paul N Adams

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

Why subscribe?

21. Other Books You May Enjoy

To get the most out of this book

You will need access to download and install open-source code packages implemented in the Python programming language and accessible through PyPi.org or the Anaconda Python distribution. While a background in statistics is helpful, but not necessary, this book assumes you have a decent background in basic algebra. Each unit of this book is independent of the other units, but the chapters within each unit build upon each other. Thus, we advise you to begin each unit with that unit’s first chapter to understand the content.

Software/hardware covered in the book	Operating system requirements
Python version ≥ 3.8	Windows, macOS, or Linux
Statsmodels 0.13.2
SciPy 1.8.1
lifelines 0.27.4
scikit-learn 1.1.1
pmdarima 2.02
Sktime 0.15.0
Pandas 1.4.3
Matplotlib 3.5.2
Numpy 1.23.0

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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Authors (3)

Huy Hoang Nguyen

Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master's in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning.

See other products by Huy Hoang Nguyen

Paul N Adams

Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University.

See other products by Paul N Adams

Stuart J Miller

Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.

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