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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

This chapter started with an introduction to time series. We provided an overview of what a time series is and how it can be used to meet specific goals. We also discussed the criteria for differentiating time-series data from data that does not depend on time. We also discussed stationarity, which factors are important for stationarity, how to measure them, and how to resolve cases where stationarity does not exist. From there, we were able to understand the primary functions of ACF and PACF analysis and for making inferences about processes using variance around the mean. Additionally, we provided an introduction to time-series modeling with an overview of the white-noise model and the basic concepts behind autoregressive and moving average components, which help form the basis of ARIMA and seasonal autoregressive integrated moving average (SARIMA) time-series models.

In Chapter 11, ARIMA Models, we will also move deeper into the discussion of autoregressive, moving average...

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Building Statistical Models in Python
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