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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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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
Author Profile Icon Paul N Adams
Paul N Adams
Stuart J Miller Stuart J Miller
Author Profile Icon Stuart J Miller
Stuart J Miller
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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

Chi-square distribution

Researchers are often faced with the need to test hypotheses on categorical data. The parametric tests covered in Chapter 4, Parametric Tests, are often not very helpful for this type of analysis. In the last chapter, we discussed using an F-test to compare sample variances. Extending that concept, we can consider the non-parametric and non-symmetric chi-square probability distribution, which is a distribution useful for comparing the means of sampling distribution variances to their population variances, specifically when the mean of a sampling distribution of sample variances is expected to equal the population variance under the null hypothesis. Because variance cannot be negative, the distribution starts at an origin of 0. Here, we can see the chi-square distribution:

Figure 5.5 – Chi-square distribution with seven degrees of freedom

Figure 5.5 – Chi-square distribution with seven degrees of freedom

The shape of the chi-square distribution does not represent an assumption that percentiles...

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