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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

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804614280
Length 420 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Huy Hoang Nguyen Huy Hoang Nguyen
Author Profile Icon Huy Hoang Nguyen
Huy Hoang Nguyen
Paul N Adams Paul N Adams
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Paul N Adams
Stuart J Miller Stuart J Miller
Author Profile Icon Stuart J Miller
Stuart J Miller
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Toc

Table of Contents (22) Chapters Close

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 21. Other Books You May Enjoy

Summary

This chapter covered topics of parametric tests. Starting with the assumptions of parametric tests, we identified and applied methods for testing the violation of these assumptions and discussed scenarios where robustness can be assumed when the required assumptions are not met. We then looked at one of the most popular alternatives to the z-test, the t-test. We iterated through multiple applications of this test, covering one-sample and two-sample versions of this test using pooling, pairing, and Welch’s non-pooled version of the two-sample analysis. Next, we explored ANOVA techniques, where we looked at using data from multiple groups to identify statistically significant differences between them. This included one of the most popular adjustments to the p-value for when a high volume of groups is present—the Bonferroni correction, which helps prevent inflating the Type I error when performing multiple tests. We then looked at performing correlation analysis...

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