Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

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)
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

Assumptions of parametric tests

Parametric tests make assumptions about population data that require the statistics practitioner to perform analysis of data prior to modeling, especially when using sample data because the sample statistics are leveraged as estimates for the population parameters when the true population parameters are unknown. These are the three primary assumptions of parametric hypothesis tests:

  • Normally distributed population data
  • Samples are independent
  • Equal population variances (when comparing two or more groups)

In this chapter, we discuss the z-test, t-test, ANOVA, and Pearson’s correlation. These tests are used on continuous data. In addition to these assumptions, Pearson’s correlation requires data to contain paired samples. In other words, there must be an equal number of samples in each group being compared as Pearson’s correlation is based on pairwise comparisons.

While these assumptions are ideal, there are...