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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

Time-to-Event Variables – An Introduction

In this short chapter, we will introduce another branch of statistics called survival analysis, which is related to survival and time-censoring studies. Survival analysis is also called time-to-event variable analysis, which is a particular statistical outcome type that requires other techniques than those used in the few last chapters that we have studied. A time-to-event variable analysis studies, for example, whether a participant has an event of interest during the study timeframe. In other words, we study the proportion of a sample surviving after a specific time point and the rate at which the survived sample proportion will fail or die, or whether there are survival differences in different treatment groups. The term survival in survival analysis is originally based on the time from treatment until death in the medical field. However, survival analysis is readily applicable to many fields including engineering (where it is referred...

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Building Statistical Models in Python
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