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Building Statistical Models in Python
Building Statistical Models in Python

Building Statistical Models in Python: Develop useful models for regression, classification, time series, and survival analysis

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Profile Icon Huy Hoang Nguyen Profile Icon Paul N Adams Profile Icon Stuart J Miller
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£26.98 £29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (11 Ratings)
eBook Aug 2023 420 pages 1st Edition
eBook
£26.98 £29.99
Paperback
£37.99
Subscription
Free Trial
Renews at £16.99p/m
Arrow left icon
Profile Icon Huy Hoang Nguyen Profile Icon Paul N Adams Profile Icon Stuart J Miller
Arrow right icon
£26.98 £29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (11 Ratings)
eBook Aug 2023 420 pages 1st Edition
eBook
£26.98 £29.99
Paperback
£37.99
Subscription
Free Trial
Renews at £16.99p/m
eBook
£26.98 £29.99
Paperback
£37.99
Subscription
Free Trial
Renews at £16.99p/m

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Building Statistical Models in Python

Sampling and Generalization

In this chapter, we will describe the concept of populations and sampling from populations, including some common strategies for sampling. The discussion of sampling will lead to a section that will describe generalization. Generalization will be discussed as it relates to using samples to make conclusions about their respective populations. When modeling for statistical inference, it is necessary to ensure that samples can be generalized to populations. We will provide an in-depth overview of this bridge through the subjects in this chapter.

We will cover the following main topics:

  • Software and environment setup
  • Population versus sample
  • Population inference from samples
  • Sampling strategies – random, systematic, and stratified
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Key benefits

  • Gain expertise in identifying and modeling patterns that generate success
  • Explore the concepts with Python using important libraries such as stats models
  • Learn how to build models on real-world data sets and find solutions to practical challenges

Description

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.

Who is this book for?

If you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

What you will learn

  • Explore the use of statistics to make decisions under uncertainty
  • Answer questions about data using hypothesis tests
  • Understand the difference between regression and classification models
  • Build models with stats models in Python
  • Analyze time series data and provide forecasts
  • Discover Survival Analysis and the problems it can solve

Product Details

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Publication date : Aug 31, 2023
Length: 420 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612156
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Product Details

Publication date : Aug 31, 2023
Length: 420 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612156
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Concepts :

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Frequently bought together


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Table of Contents

21 Chapters
Part 1:Introduction to Statistics Chevron down icon Chevron up icon
Chapter 1: Sampling and Generalization Chevron down icon Chevron up icon
Chapter 2: Distributions of Data Chevron down icon Chevron up icon
Chapter 3: Hypothesis Testing Chevron down icon Chevron up icon
Chapter 4: Parametric Tests Chevron down icon Chevron up icon
Chapter 5: Non-Parametric Tests Chevron down icon Chevron up icon
Part 2:Regression Models Chevron down icon Chevron up icon
Chapter 6: Simple Linear Regression Chevron down icon Chevron up icon
Chapter 7: Multiple Linear Regression Chevron down icon Chevron up icon
Part 3:Classification Models Chevron down icon Chevron up icon
Chapter 8: Discrete Models Chevron down icon Chevron up icon
Chapter 9: Discriminant Analysis Chevron down icon Chevron up icon
Part 4:Time Series Models Chevron down icon Chevron up icon
Chapter 10: Introduction to Time Series Chevron down icon Chevron up icon
Chapter 11: ARIMA Models Chevron down icon Chevron up icon
Chapter 12: Multivariate Time Series Chevron down icon Chevron up icon
Part 5:Survival Analysis Chevron down icon Chevron up icon
Chapter 13: Time-to-Event Variables – An Introduction Chevron down icon Chevron up icon
Chapter 14: Survival Models Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(11 Ratings)
5 star 90.9%
4 star 9.1%
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1 star 0%
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Dror Oct 01, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Statistics is a fundamental discipline concerned with the collection, organization, analysis, interpretation, and presentation of data. While Python—an extremely popular general-purpose programming language—has become the programming language of choice for computation in most science and engineering disciplines, most (software-oriented) statistics books still teach statistics using the more special-purpose R language.This unique and highly practical book provides a gentle introduction to statistics and to using the Python programming language for building statistical models. It begins with a clear and useful introduction to statistics, including sampling, data distributions, hypothesis testing, and parametric and non-parametric statistical tests. It then progresses to describe in detail how to build statistical models using Python for a variety of problems, including for regression, classification, time-series, and survival analysis. The descriptions are clear and concise, and gradually present additional common and helpful Python packages for performing statistical analysis. The accompanying GitHub repository includes practical and detailed code examples, and is very helpful in reinforcing the materials and concepts presented in the book.I highly recommend this book to anyone interested in learning statistics and how to use Python for building statistical models. It requires no more than basic knowledge of the Python programming language, and will be ideal for data scientists, analysts, and industry professionals who are taking their first steps in the world of statistics or want to expand their knowledge in this area.Highly recommended!
Amazon Verified review Amazon
JRVV Oct 19, 2023
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The book provides a broad primer on statistical modeling using Python. This book can also serve as a starting point to those who eventually want to go into machine learning. Recommended.
Amazon Verified review Amazon
Amazon Customer Oct 02, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is exceptionally crafted, serving as a comprehensive review of fundamental statistical knowledge, complemented with practical Python codes. Unlike other market options, which either focus solely on theory or coding, lacking depth in theoretical insight, this book seamlessly bridges theory to application. While many statistical texts predominantly utilize the R language, this book's emphasis on Python is a refreshing change. It not only rejuvenates and reinforces my existing knowledge but also significantly advances my understanding of Statistics and Machine Learning. It stands out as a balanced and insightful resource for both theoretical comprehension and practical application in the field.
Amazon Verified review Amazon
Steven Fernandes Oct 09, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The authors offer a compelling dive into making informed decisions under uncertainty, equipping readers with practical skills, such as hypothesis testing and data analysis. They thoughtfully elucidate the distinctions between regression and classification models and provide a hands-on approach to building models using Python's statsmodels. The text also insightfully explores time-series data analysis, forecasting, and survival analysis, adeptly linking theory with real-world applications. This book emerges as an invaluable guide for both beginners and seasoned practitioners, intertwining robust theoretical constructs with practical applicability in data analysis and model-building, rendering it a must-read in the field of data science.
Amazon Verified review Amazon
Ratan Nov 23, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Authors have done a good job in maintaining the comprehensiveness of the book. They have maintained adequate amount of mathematics what is needed. I particularly loved the way they have presented Hypothesis testing for models which is often missing in many places. They have nicely covered both parametric and non parametric testing.The other part I liked was somewhat less visited topic survival analysis. Overall I found this book an excellent read ! Definitely recommend it.
Amazon Verified review Amazon
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