Learn basic statistical concepts in the context of analytics, resampling, and the bootstrap with this concise and accessible primer. Featuring over 300 "Try It Yourself" exercises and interactive links, this book emphasizes statistical literacy and understanding to demystify traditional formulas. Ideal for introductory statistics courses and as a supplement for upper-level statistics and related fields.

A Resampling Perspective

Introductory Statistics and Analytics

Master the complex subject of data analysis with this tutorial guide. Intended for senior undergraduates and research students in science and engineering, this book employs a logical and unified approach to Bayesian probability theory, illustrated by real-world examples. Topics also include reliability analysis, hypothesis testing, and experimental design. The second edition features a new chapter on outlier handling and a contribution on the latest numerical technique for Bayesian computation. Let this book be your key to unlocking the mysteries of statistical analysis.

A Bayesian Tutorial

Data Analysis

Discover the world of data mining and machine learning with this comprehensive guide. Written by three prominent professors of statistics, this book provides a common conceptual framework for understanding the tools and ideas in various fields such as medicine, biology, finance, and marketing. With a focus on concepts rather than mathematics, it covers a broad range of topics including neural networks, support vector machines, classification trees, and boosting. With many examples and color graphics, this is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Data Mining, Inference, and Prediction (Springer Series in Statistics)

The Elements of Statistical Learning

Learn how to avoid common statistical fallacies and analyze data correctly with "Statistics Done Wrong". This book offers cautionary tales of mistakes made by even the most knowledgeable scientists and teaches you the best practices for avoiding those errors. Discover the ideas behind hypothesis testing and regression analysis, how to ask the right questions, design experiments, and work with data. Through colorful examples of statistics gone awry, this book offers approachable lessons for proper methodology, and pro tips for practicing scientists and statisticians. Improve your data analysis skills with "Statistics Done Wrong".

The Woefully Complete Guide

Statistics Done Wrong

This book is a comprehensive guide to predictive modeling for both beginners and practitioners. It offers intuitive explanations of various regression and classification techniques, with practical examples and emphasis on solving real data problems. The authors bring years of experience in the pharmaceutical and diagnostic industries, providing valuable insights and advice on the predictive modeling process. While a mathematical background is helpful for advanced topics, the text is approachable for non-mathematical readers. Perfect for those looking to extend their expertise in predictive modeling.

Applied Predictive Modeling

Learn how to turn data into knowledge with an introduction to statistical analysis using Python. By working through a single case study, this book teaches the entire process of data analysis, from collecting data to identifying patterns and testing hypotheses. Discover distributions, rules of probability, visualization, and more through writing and testing code. With new chapters on regression, time series analysis, survival analysis, and analytic methods, gain a deeper understanding of statistical inference to answer real-world questions.

Exploratory Data Analysis

Think Stats

"Data Analysis Using Regression and Multilevel Models" is an essential guide for applied researchers looking to analyze data with linear and nonlinear regression and multilevel models. Filled with real-world examples and programming codes, this comprehensive manual covers topics such as regression discontinuity, matching, and causal inference. Learn how to fit models using available software packages and gain practical tips on building, fitting, and understanding data.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Learn probability and statistics quickly with this book, covering a broad range of topics beyond typical introductory math books. Perfect for graduate or advanced undergrads in computer science, math, statistics, and related disciplines. Get familiar with modern topics like nonparametric curve estimation, bootstrapping, and classification, without needing prior knowledge. Discover the intersection of statistics, data mining, and machine learning, and how statisticians and computer scientists are working together to advance these fields.

A Concise Course in Statistical Inference (Springer Texts in Statistics)

All of Statistics

This comprehensive book covers Data Analysis techniques used in Commerce, Economics, Business, and Science. Readers will learn about Descriptive and Inferential Statistics, Probability, Random Sampling, and Regression Analysis. In addition, the book includes 4 Trial Exams with detailed solutions.

INTRODUCTION TO DATA SCIENCE

PROBABILITY & STATISTICS

A comprehensive study guide for students pursuing degrees in Business Analytics, Data Science, and Statistics. Packed with Essential Theory, Solved Problems, Supplementary Questions, Trial Exam Questions, and Exam Revision materials. Perfect for first-year students in college or university.

Business Analytics Study Guide

Learn the basics of data science with this easy-to-follow introduction. Perfect for first year degree or diploma students, this book covers essential theory, includes solved problems, supplementary questions, and trial exams. Ideal for those looking to gain a solid understanding of analytics.

ANALYTICS

Learn the ins and outs of Statistical Research Methods with this expert guide by Roy Sabo. Non-specialists will appreciate the user-friendly introduction and real-life examples that take readers from hypothesis generation to manuscript writing. Perfect for those looking to dive into data analysis.

A Guide for Non-Statisticians

Statistical Research Methods

Статистические методы являются ключевой частью науки о данных. Однако очень немногие аналитики данных обучены статистике должным образом, поскольку нет книг по статистике, написанных специально для аналитиков данных.
С другой стороны, многие ресурсы, посвященные науке о данных, содержат статистические методы, но не раскрывают перспективы применения этих методов достаточно глубоко.
Предлагаемая книга, написанная доступным языком, устраняет этот пробел. Если вы немного знакомы с языком программирования R и математической статистикой, то легко освоите материал и существенно повысите свой профессиональный уровень
Во второе издание включены примеры на языке Python, что расширяет практическое применение книги.

50+ Essential Concepts Using R and Python

Практическая статистика для специалистов Data Science

Discover a balanced approach to mathematical statistics with Modern Mathematical Statistics with Applications, 2nd Edition. This book strikes a balance between rigorous mathematical development and statistical practice by providing clear explanations and a wide range of applications involving real data. With a focus on inferential statistics, readers will learn about point estimation, statistical intervals, hypothesis testing, and more, with new exercises that prepare students for actuarial exams. Explore how to apply this methodology to real-world scenarios, including predicting O-ring failure during space shuttle launches and assessing if the average tip percentage in a restaurant exceeds 15%.

Modern Mathematical Statistics with Applications

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.