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  • Book Overview & Buying Building Statistical Models in Python
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Building Statistical Models in Python

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
4.9 (11)
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Building Statistical Models in Python

Building Statistical Models in Python

4.9 (11)
By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
Table of Contents (22 chapters)
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1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

Non-Parametric Tests

In the previous chapter, we discussed parametric tests. Parametric tests are useful when test assumptions are met. However, there are cases where those assumptions are not met. In this chapter, we will discuss several non-parametric alternatives to the parametric tests presented in the previous chapter. We start by introducing the concept of a non-parametric test. Then, we will discuss several non-parametric tests that can be used when t-test or z-test assumptions are not met.

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

  • When parametric test assumptions are violated
  • The rank-sum test
  • The signed-rank test
  • The Kruskal-Wallis test
  • The chi-square test
  • Spearman’s correlation analysis
  • Chi-square power analysis
CONTINUE READING
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Tech Concepts
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Programming languages
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Building Statistical Models in Python
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