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Chapman & Hall/CRC Texts in Statistical Science


About the Series

For more than a quarter of a century, this internationally recognized series has fostered the growth of statistical science by publishing upper level textbooks of high quality at reasonable prices. These texts, which cover new frontiers as well as developments in core areas, continue to have a major role in shaping the discipline through the education of young scientists both in statistics as well as in fields wherein the role of statistics is becoming increasingly important.

The series covers a very broad domain. Students in upper level undergraduate and graduate courses in biostatistics, epidemiology, probability and statistics will constitute the primary readership for the series. However, others in areas such as engineering, life science, business, environmental science and social science will find books of interest. Scientists in these areas will also find useful references since emphasis is placed on readability, real examples and case studies, and on tying theory into relevant software such as SAS, Stata, and R.

Please contact us if you have an idea for a book for the series.

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Analysis of Longitudinal Studies in Epidemiology

Analysis of Longitudinal Studies in Epidemiology

1st Edition

Forthcoming

By Nicholas P. Jewell, Alan Hubbard
April 18, 2022

Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and ...

Practical Time Series Analysis for Data Science

Practical Time Series Analysis for Data Science

1st Edition

Forthcoming

By Wayne A. Woodward, Bivin Philip Sadler, Stephen Robertson
April 13, 2022

Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Practical Time Series Analysis for Data Science discusses techniques of ensemble modelling for combining information from several strategies. Covering time...

Introduction to Design and Analysis of Experiments and Observational Studies using R

Introduction to Design and Analysis of Experiments and Observational Studies using R

1st Edition

Forthcoming

By Nathan Taback
March 22, 2022

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data ...

Stochastic Processes with R An Introduction

Stochastic Processes with R: An Introduction

1st Edition

Forthcoming

By Olga Korosteleva
February 17, 2022

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is...

Bayes Rules! An Introduction to Applied Bayesian Modeling

Bayes Rules!: An Introduction to Applied Bayesian Modeling

1st Edition

Forthcoming

By Alicia A. Johnson, Miles Q. Ott, Mine Dogucu
January 25, 2022

An engaging, sophisticated, and fun introduction to the field of Bayesian Statistics, Bayes Rules! An Introduction to Bayesian Modeling with R brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate...

Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python

1st Edition

Forthcoming

By Osvaldo A. Martin, Ravin Kumar, Junpeng Lao
December 29, 2021

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying ...

Theory of Statistical Inference

Theory of Statistical Inference

1st Edition

Forthcoming

By Anthony Almudevar
December 22, 2021

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference, and would be suitable for a core course in a ...

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists: With R and Python

1st Edition

By Alan Agresti, Maria Kateri
November 30, 2021

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data ...

Sampling Design and Analysis

Sampling: Design and Analysis

3rd Edition

By Sharon L. Lohr
November 30, 2021

"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor ...

Probability, Statistics, and Data A Fresh Approach Using R

Probability, Statistics, and Data: A Fresh Approach Using R

1st Edition

By Darrin Speegle, Bryan Clair
November 26, 2021

This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions ...

Fundamentals of Causal Inference With R

Fundamentals of Causal Inference: With R

1st Edition

By Babette A. Brumback
November 10, 2021

One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of ...

A First Course in Linear Model Theory

A First Course in Linear Model Theory

2nd Edition

By Nalini Ravishanker, Zhiyi Chi, Dipak K. Dey
October 19, 2021

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an ...

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