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Book Overview & Buying
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Table Of Contents
Building Statistical Models in Python
By :
Building Statistical Models in Python
By:
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)
Preface
Part 1:Introduction to Statistics
Chapter 1: Sampling and Generalization
Chapter 2: Distributions of Data
Chapter 3: Hypothesis Testing
Chapter 4: Parametric Tests
Chapter 5: Non-Parametric Tests
Part 2:Regression Models
Chapter 6: Simple Linear Regression
Chapter 7: Multiple Linear Regression
Part 3:Classification Models
Chapter 8: Discrete Models
Chapter 9: Discriminant Analysis
Part 4:Time Series Models
Chapter 10: Introduction to Time Series
Chapter 11: ARIMA Models
Chapter 12: Multivariate Time Series
Part 5:Survival Analysis
Chapter 13: Time-to-Event Variables – An Introduction
Chapter 14: Survival Models
Index