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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

Summary

In this chapter, we discussed the concept of MLR and topics aiding in its implementation. These topics included feature selection methods, shrinkage methods, and PCR. Using these tools, we were able to demonstrate approaches to reduce the risk of modeling excess variance. In doing so, we were able to also induce model bias so that models can have a better chance of generalizing on unseen data with minimal complications as frequently faced when overfitting.

In the next chapter, we will begin a discussion on classification with the introduction of logistic regression, which fits a sigmoid to a linear regression model to derive probabilities of binary class membership.

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Building Statistical Models in Python
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