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

ARIMA Models

In this chapter, we will discuss univariate time series models. These are models that only consider a single variable and create forecasts based only on the previous samples in the time series. We will start by looking at models for stationary time series data and then progress to models for non-stationary time series data. We will also discuss how to identify appropriate models based on the characteristics of time series. This will provide a powerful set of models for forecasting time series.

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

  • Models for stationary time series
  • Models for non-stationary time series
  • More on model evaluation