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Building Statistical Models in Python

You're reading from Building Statistical Models in Python Develop useful models for regression, classification, time series, and survival analysis

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781804614280

Length 420 pages

Edition 1st Edition

Languages

Python

Concepts

Statistics

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 FREE CHAPTER

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

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21. Other Books You May Enjoy

Parametric Tests

In the previous chapter, we introduced the concept of a hypothesis test and showed several applications of the z-test. The z-test is a type of hypothesis test in a family of hypothesis tests called parametric tests. Parametric tests are powerful hypothesis tests, but the application of parametric tests requires certain assumptions to be met by the data. While the z-test is a useful test, it is limited by the required assumptions. In this chapter, we will discuss several more parametric tests, which will expand our parametric tool set. More specifically, we will discuss the various applications of the t-test, how to perform tests when more than two subgroups of data are present, and the hypothesis test for Pearson’s correlation coefficient. We will complete the chapter with a discussion on power analysis for parametric tests.

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

Assumptions of parametric tests
T-test—a...

The rest of the chapter is locked

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Authors (3)

Huy Hoang Nguyen

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.

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Stuart J Miller

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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Paul N Adams

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.

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