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

Summary

In this chapter, we discussed three survival analysis models in depth; the Kaplan-Meier, the exponential, and the Cox Proportional Hazards regression models. Using these frameworks, we modeled survival functions and estimated survival probabilities and hazard ratios for various TTE, right-censored studies. For the multivariate case, we used Cox Proportional Hazards regression to model hazard ratios for covariate analysis on dependent variables. For all models, we demonstrated using the confidence intervals for assessing significance, as well as the corresponding p-values. At this point, the reader should be able to confidently identify the scenarios in which each model would outperform the others and appropriately fit and implement that model to obtain the necessary results for strategic success.