This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics.

This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

This book brings a modern flavor to the course, incorporating the computer and offering an integrated approach to inference that includes the frequency approach and the Bayesian inference.

This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, and focuses on real engineering applications, as well as basics of sampling distributions, estimation and hypothesis testing.

This book presents an introduction to probability and mathematical statistics for students already having some elementary mathematical background. It blends proven coverage with new innovations to ensure you gain a solid understanding of statistical concepts.

An excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical statistical modelling and computational problems. Understanding of quantitative methods and apply to real world apps.

The aim of the book is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.

This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains hands-on exercises with real data - mostly from social sciences. It presents four key ingredients of statistical data analysis.

Build a solid foundation in data analysis, This guide starts with an overview of statistics and why it is so important. Be confident that you understand what your data are telling you and that you can explain the results to others!

This book takes readers on a journey of statistical discovery using the freeware R, and is written in an irreverent style and follows the same ground breaking structure and pedagogical approach.

This book provides a compact introduction to the Bootstrap Method. It is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form.

Statistical thinking is increasingly essential to making informed decisions based on uncertain data. This book provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data.

This book offers a detailed, illustrated breakdown of the fundamentals of statistics. Develop and use formal logical thinking abilities to understand the message behind numbers and charts in science, politics, and economy.

The book gives a rigorous treatment of the elementary concepts in statistical inference from a classical frequentist perspective. The ideal readers are quantitatively literate and have a basic understanding of statistical concepts and R programming.

This book is a general introduction to the statistical analysis of networks. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, etc.

This open access book discusses the statistical modeling of insurance problems. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.

This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems. With examples in R and Python.

This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

This book provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, etc.

This book is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (GAISE).

This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action.

This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

This book aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services.

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.

This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

This is a practical book. It is aimed at people who need to understand statistics, but not develop it as a subject. The typical reader might be a postgraduate student in health, life, or social science who has no knowledge of statistics.

The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

The emphasis of this book is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.

This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Become competent at implementing Regression Analysis in Python Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

It 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 describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.

This book provides a cross-disciplinary forum for exploring the variety of new data analysis techniques emerging from different fields, focusing on recent advances in data analysis techniques in many different research fields.

Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

It uses probabilistic programming languages to express such Bayesian procedural models. A probabilistic programming language (PPL) provides random choice and Bayesian conditioning operators as primitives, and inference.

This book is a self contained introduction to classical statistical theory. The material is suitable for students who have successfully completed a single year's course in calculus with no prior knowledge of statistics or probability.

This e-book is a complete interactive study guide with quizzing functionality that reports to the instructor. The on-line text also has animated figures and graphs that bring the print graphic to life for deeper understanding.

The goal of this book is to make statistics more accessible through interactive visualizations (using D3.js). It visualizes the fundamental concepts covered in an introductory college statistics or Advanced Placement statistics class.

This is an introduction to statistics, with R, without calculus, for students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

This book is geared to the high school audience and is specifically tailored to be aligned with the AP Statistics curriculum. It is already being used by many high schools and community colleges throughout the country.

This book provides the foundational bricks and mortar needed to master the theories and methodologies behind statistical procedures. In just a little more than 100 pages, you'll understand how to better gather and interpret all the information at your fingertips.

This book is intended for introductory statistics courses being taken by students at two- and four-year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite.

The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly.

This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

This book aims to provide the reader with an entry point to the voluminous literature on statistical network modeling. It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities.

This book explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software.

This book is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis, provides data analysis examples, R code, computer output, and explanation of results.

Build efficient forecasting models using traditional time series models and machine learning algorithms. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts. Perform time series analysis and forecasting confidently with Python.

A unique feature of this book is its integration with the statistical software package SAS® (Statistical Analysis System) computing environment. Basic applied statistics is assumed through multiple regression.

This book provides conceptual information about SAS® software along with step-by-step examples that illustrate the concepts. It answers every question that a new or intermediate SAS® user might have. Examples are thorough, readable, and clear.

Gain insight on SAS® solutions and analytics technology with a collection of free e-books, includes Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, Programming for R Users, etc.

This book is intended to introduce the concepts, definitions, and terminology of the subject in an elementary presentation with minimum mathematical background which does not surpass college algebra.

Approaching an introductory statistical inference textbook in a novel way, this book walks through a simple introduction to probability, and then applies those principles to all problems of inference.

Spatial data is crucial to improve decision-making in a wide range of fields. This book describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R.

Spatial statistics has been widely used in many environmental studies. This book is for people who are interested in applying statistics to spatial data, as well as for who know statistics and want to explore how it can be applied to spatial data.

This book is intended to serve as both a graduate-level textbook and a technical reference. The focus is on fundamental concepts, analytical techniques, and basic empirical methods. The only prerequisite is an introductory course on Fourier analysis.

This book introduces students to probability, statistics, and stochastic processes. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy.

This book illustrates the capabilities of Microsoft Excel to teach applied statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems

With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics.

This is a textbook for a course in mathematical probability and statistics for computer science students. Computer science examples are used throughout, in areas such as: computer networks; data and text mining; computer security; etc.

This is a collection of lectures on probability theory and mathematical statistics. It is offered as a free service to the mathematical community and provides an accessible introduction to topics that are not usually found in elementary textbooks.

This book is both a tutorial and a textbook. It presents an introduction to probability and mathematical statistics and it is intended for students already having some elementary mathematical background.

This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, and focuses on real engineering applications, as well as basics of sampling distributions, estimation and hypothesis testing.

Written by a renowned statistician, this book presents the basic ideas behind the statistical methods commonly used in studies of human subjects. Accessible to a wide audience with little background in statistics.

This is a textbook for an undergraduate course in probability and statistics. Calculus and some linear algebra knowledge is required.

Long the province of mathematicians and statisticians, Bayesian methods are applied in this ground-breaking book to problems in cutting-edge physics, with practical examples of Bayesian analysis for the physicist working in such areas as neural networks, artificial intelligence, and inverse problems in quantum theory.

This book takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required.

A straightforward introduction to the statistical analysis of language, designed for those with a non-mathematical background. Using the leading statistics programme 'R'. Suitable for all those working with quantitative language data.

It provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics, via code examples with liberal commenting of the code and the output, from the computational and the statistical viewpoint.

This book provides a long-needed systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervised-learning problem for both regression and classification.

The aim of this book is to present the statistical and mathematical principles of smoothing with a focus on applicable techniques. The necessary mathematical treatment is easily understandable and a wide variety of interactive smoothing examples are given.

This book focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds.

This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.

This book gives a broad and modern introduction to statistics for multivariate data. A bunch of interesting examples is used to illustrate the techniques.

The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique.

This book exposes the broad range of applications of Monte Carlo simulation in the fields of Quantum Physics, Statistical Physics, Reliability, Medical Physics, Polycrystalline Materials, Ising Model, Chemistry, Agriculture, Food Processing, X-ray Imaging, ...

This book presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance.

This book is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

This book offers a comprehensive, almost encyclopedic presentation of statistical methods and analytic approaches used in science, industry, business, and data mining, written from the perspective of the real-life practitioner ('consumer') of these methods.

Help scientists and engineers incorporate statistical methods in their work as efficiently as possible. Many parts of the book feature case studies or short examples with computations from Dataplot, the free, downloadable software.

This book is an introduction to statistics for students of economics or business, suitable for a one or two-semester course, yet uses the simplest mathematics consistent with a sound presentation.

This book provides the first general framework, based on universal statistical properties of natural images, of detecting tampering and authenticating digital images that has been successfully applied to three problems in digital image forensics.

This book describes the essential tools and techniques of statistical signal processing, with specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, etc.

This book presents an exhaustive and detailed account of Window Functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing.

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, etc..

Statistical, machine learning and neural network approaches to classification are all covered in this book to provide an objective assessment of the potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms.

A practical guide to geostatistical mapping using R+gstat/geoR, SAGA GIS and Google Earth combo of software packages.

This handbook cover the important subareas of computational statistics and give some flavor of the wide range of applications.

This book lays the foundation for further study and development in statistics using R - an ideal text for integrating the study of statistics with a powerful computational tool.

This book provides an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis.

This book is both an introductory textbook and a research monograph on modelling the statistical structure of natural images. The statistical structure is described using a number of statistical models whose parameters are estimated from image samples.