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Applied Event-Related Potential Data Analysis
Steven J Luck · University of California, Davis
The purpose of this book is to provide you with a practical overview of the data processing operations that are used to extract the ERPs from the EEG. This includes preprocessing operations that reduce noise and artifacts, the signal averaging operation at the core of extracting the ERPs, and the postprocessing operations that are used to convert the ERP waveforms into a small set of variables for statistical analysis.
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Spreadsheet software for engineering: an applied guide
Julio Terán & Phillip Chvosta · North Carolina State University
Có trong mục lục: 4.04: Applied Example
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Big Data for Epidemiology: Applied Data Analysis Using National Health Surveys
Tiffany Kindratt · Mavs Open Press · 2022
National data sets provide an avenue for students to practice data analytic skills while also answering meaningful research questions. This open education resource was developed to train future public health professionals how to conduct secondary data analysis of national health surveys using SAS statistical software. SAS software was selected because it is one of the most commonly used software programs used among public health departments and academia. The book includes details on how to analyze public-use data from five common national health surveys, including the National Health Interview Survey (NHIS), Medical Expenditure Panel Survey (MEPS), Health Information National Trends Survey (HINTS), Behavior Risk Factor Surveillance System (BRFSS) and National Health and Nutrition and Examination Survey (NHANES). All datasets and corresponding syntax files are available from the Open ICPSR Data Repository.
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Data Analysis in the Psychological Sciences: A Practical, Applied, Multimedia Approach
Leyre Castro; J. Toby Mordkoff · University of Iowa Libraries · 2023
This open resources textbook contains 10 Units that describe and explain the main concepts in statistical analysis of psychological data. In addition to conceptual descriptions and explanations of the basic analyses for descriptive statistics, this textbook also explains how to conduct those analyses with common statistical software (Excel) and open-source free software (R).
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Answering Questions with Data - Introductory Statistics for Psychology Students
Matthew J. C. Crump · Brooklyn College of CUNY
This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.
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Social Data Analysis: Qualitative and Quantitative Approaches
Mikaila Mariel Lemonik Arthur and Roger Clark · Rhode Islange College
This book is meant to help you in your initial attempts to analyze data. In doing so it will introduce you to ways that others have found useful in their attempts to organize data. You might think of it as like a recipe book, a resource that you can refer to as you prepare data for your own consumption and that of others. And, like a recipe book that teaches you to prepare simple dishes, you may find this one pretty exciting.
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Answering questions with data: Introductory Statistics for Psychology Students
Matthew Crump · Crump Lab · 2018
This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.
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The Data Journalism Handbook
Liliana Bounegru; Lucy Chambers; Jonathan Gray · European Journalism Centre · 2012
When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both.
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Practical Game Programming
Harris Wang; Walter Ridgewell · Athabasca University Press · 2026
Practical Game Programming provides a structured, accessible introduction to game development using C++ and the Allegro 5 library. Designed for upper-level undergraduate learners, it guides students through the complete development pipeline—from programming fundamentals and user input to graphics, audio, file management, and game timing. Advanced topics such as collision detection, multithreading, optimization, custom shaders, and networking are covered in depth. This OER emphasizes both technical skills and design principles, including game conception, prototyping, UI/UX, testing, and quality assurance. Each chapter includes exercises and projects, culminating in genre-based development challenges across action, platformer, adventure, arcade, puzzle, role-playing, strategy, and more. With a focus on applied learning and creative exploration, this text equips students to build professional-quality games and prepares them for real-world development environments.
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Discrete Mathematics for Information Technology
Simon Crothers · Kwantlen Polytechnic University · 2026
Discrete Mathematics for Information Technology is an applied, student-focused introduction to the mathematical foundations that underpin modern computing. Designed for learners in information technology and business contexts, this textbook bridges theory and practice by connecting core concepts, such as number systems, data representation, logic, proofs, relations, algorithms, Boolean algebra, and graph theory, to real-world applications in programming, networking, databases, and systems design. Each chapter is structured with clear explanations, worked examples, case studies, and programming-oriented exercises that reinforce both conceptual understanding and practical skills. Emphasis is placed on developing analytical thinking, problem-solving, and the ability to model and reason about digital systems with precision and confidence. As an open educational resource, the text is adaptable and accessible, supporting a wide range of teaching and learning environments. By integrating mathematical rigor with authentic IT scenarios, this book equips students with the essential tools needed to succeed in modern, data-driven and technology-focused careers.
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Computer Systems Security: Planning for Success
Ryan Tolboom · Ryan Tolboom
The text, labs, and review questions in this book are designed as an introduction to the applied topic of computer security. With these resources students will learn ways of preventing, identifying, understanding, and recovering from attacks against computer systems. This text also presents the evolution of computer security, the main threats, attacks and mechanisms, applied computer operation and security protocols, main data transmission and storage protection methods, cryptography, network systems availability, recovery, and business continuation procedures.
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Evidence-based Software Engineering
Derek Jones · Knowledge Software · 2020
This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.
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Introduction to Statistical Thinking
Benjamin Yakir · Benjamin Yakir · 2010
The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think. Teaching statistics is a challenge. Teaching it to students who are required to learn the subject as part of their curriculum, is an art mastered by few. In the past I have tried to master this art and failed. In desperation, I wrote this book. This book uses the basic structure of generic introduction to statistics course. However, in some ways I have chosen to diverge from the traditional approach. One divergence is the introduction of R as part of the learning process. Many have used statistical packages or spreadsheets as tools for teaching statistics. Others have used R in advanced courses. I am not aware of attempts to use R in introductory level courses. Indeed, mastering R requires much investment of time and energy that may be distracting and counterproductive for learning more fundamental issues. Yet, I believe that if one restricts the application of R to a limited number of commands, the benefits that R provides outweigh the difficulties that R engenders. Another departure from the standard approach is the treatment of probability as part of the course. In this book I do not attempt to teach probability as a subject matter, but only specific elements of it which I feel are essential for understanding statistics. Hence, Kolmogorov’s Axioms are out as well as attempts to prove basic theorems and a Balls and Urns type of discussion. On the other hand, emphasis is given to the notion of a random variable and, in that context, the sample space.
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Fundamentals of MATLAB Programming
Zara Noroozi · Irvine Valley College
Có trong mục lục: 12: Applied Mathematics · 12.02: Why Applied Mathematics Matters in MATLAB