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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

Multiple Linear Regression

In the last chapter, we discussed simple linear regression (SLR) using one variable to explain a target variable. In this chapter, we will discuss multiple linear regression (MLR), which is a model that leverages multiple explanatory variables to model a response variable. Two of the major conundrums facing multivariate modeling are multicollinearity and the bias-variance trade-off. Following an overview of MLR, we will provide an induction into the methodologies used for evaluating and minimizing multicollinearity. We will then discuss methods for leveraging the bias-variance trade-off to our benefit as analysts. Finally, we will discuss handling multicollinearity using Principal Component Regression (PCR) to minimize overfitting without removing features but rather transforming them instead.

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

  • Multiple linear regression
  • Feature selection
  • Shrinkage methods
  • Dimension...
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83
Tech Concepts
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Programming languages
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Building Statistical Models in Python
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