Digital library
Textbooks, references and open learning resources.
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The Stories Behind Social Statistics: Data Analysis, Interpretation, and Communication
Ezra Temko · Southern Illinois University Edwardsville
This text presents introductory social statistics concepts and techniques and how to use SPSS software.
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Applied Event-Related Potential Data Analysis
Steven J Luck · University of California, Davis
In the contents: 3.08: Exercise- Simple Statistical Analysis of N400 Data · 3.09: Exercise- A More Complex Analysis · 6.10: Exercise - Sequential Analysis of the P3b
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Social Data Analysis: Qualitative and Quantitative Approaches
Mikaila Mariel Lemonik Arthur and Roger Clark · Rhode Islange College
In the contents: 01: Introducing Social Data Analysis · 1.01: Introducing Social Data Analysis · 02: Quantitative Data Analysis
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Statistical Analysis of Data for Engineers
Joshua Steimel · Cal Poly Humbolt
In the contents: Chapter 09: Analysis of Variance (ANOVA)
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Algorithm Design and Analysis
Godfry Justo · African Virtual University
In the contents: 01: Fundamental Design and Analysis Techniques · 1.01: Activity 1 - Overview of Algorithm Design and Analysis
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Social Data Analysis
Mikaila Arthur; Roger Clark · Rhode Island College Digital Publishing · 2021
Social data analysis enables you, as a researcher, to organize the facts you collect during your research. Your data may have come from a questionnaire survey, a set of interviews, or observations. They may be data that have been made available to you from some organization, national or international agency or other researchers. Whatever their source, social data can be daunting to put together in a way that makes sense to you and others. 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. Analyzing data in a revealing way is at least as rewarding, we’ve found, as it is to cook up a yummy cashew carrot paté or a steaming corn chowder. We’d like to share our pleasure with you.
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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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Basic Engineering Data Collection and Analysis
Steve Vardeman; J. Marcus Jobe · Iowa State University · 2001
In Basic Engineering Data Collection and Analysis, Stephen B. Vardeman and J. Marcus Jobe stress the practical over the theoretical. Step by step, students get real engineering data and scenario examples along with chapter-long case studies that illustrate concepts in realistic, thoroughly detailed situations. This approach encourages students to work through the material by carrying out data collection and analysis projects from problem formulation through the preparation of professional technical reports—just as if they were on the job.
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First Semester in Numerical Analysis with Python
Yaning Liu · Auraria Institutional Repository
The book is based on “First semester in Numerical Analysis with Julia”, written by Giray Ökten. The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index, a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based interactive development environment for Python as well as many other programming languages, was used throughout the book and is recommended to the readers for easy code development, graph visualization and reproducibility.
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Introduction to the Modeling and Analysis of Complex Systems
Hiroki Sayama · Open SUNY · 2015
Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.
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A Brief Introduction to Engineering Computation with MATLAB
Serhat Beyenir · British Columbia Institute of Technology
In the contents: 1.07: Regression Analysis
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Open Data Structures - An Introduction
Pat Morin · Carleton University
In the contents: 4.04: Analysis of Skiplists
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Geographic Information Systems and Cartography
Adam Dastrup · Salt Lake Community College
In the contents: 07: Vector Data Analysis · 7.02: Single Layer Analysis · 7.03: Multiple Layer Analysis
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Data Structures
Wikipedia · LibreTexts
This book is about the creation and analysis of efficient data structures. To understand the material in this book you should be comfortable enough in a programming language to be capable of working with and writing your own variables, arithmetic expressions, if-else conditions, loops, subroutines (also known as functions), pointers (also known as references or object handles), structures (also known as records or classes), simple input and output, and simple recursion.
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CISP 430: Data Structures
Suha Aljuboori · Folsom Lake College
This course applies object-oriented techniques for systematic problem analysis and the managing of program complexity using abstraction. Specifications, design, coding, testing, and documentation of large multi-file programs are covered. It uses advanced language features such as classes, strings, non-text files, pointers, and recursion. Abstract data types such as stacks, queues, lists, binary trees, heaps/priority queues, hash tables, and graphs are examined.
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CPSI10003 Microcomputer Applications 1 - Microsoft Excel
LueCrecy Ragan · Arkansas State University-Beebe
In the contents: 2.1.05: What-If Analysis
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Web Writing
Amber Pfannenstiel · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2023
Web Writing (2023) by Dr. A Nicole Pfannenstiel is designed to provide a practical, rhetorical approach to web writing and content strategy analysis for students completing advanced writing courses. The eTextbook uses the rhetorical situation and key concepts to help readers/students understand how to write within specific web spaces for specific audiences drawing on appropriate discourse community conventions. It includes a chapter devoted to the rhetorical situation and key concepts to help students analyze and build their understanding of existing communication. It also includes a chapter outlining approaches to content strategy analysis, using the rhetorical situation and key concepts to understand the rich public data provided through social media accounts to support learners understanding effective web writing. The content analysis overview helps students build skills for analyzing writing, for collecting and analyzing qualitative and quantitative social media data, and for drawing conclusions about content strategy best practices.
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Dynamic data processing: Recursive least-squares
Peter Teunissen · TU Delft Open · 2024
This book is a follow-up on Adjustment theory. It extends the theory to the case of time-varying parameters with an emphasis on their recursive determination. Least-squares estimation will be the leading principle used. A least-squares solution is said to be recursive when the method of computation enables sequential, rather than batch, processing of the measurement data. The recursive equations enable the updating of parameter estimates for new observations without the need to store all past observations. Methods of recursive least-squares estimation are therefore particularly useful for applications in which the time-varying parameters need to be instantly determined. Important examples of such applications can be found in the fields of real-time kinematic positioning, navigation and guidance, or multivariate time series analysis. The goal of this book is therefore to convey the necessary knowledge to be able to process sequentially collected measurements for the purpose of estimating time-varying parameters. When determining time-varying parameters from sequentially collected measurement data, one can discriminate between three types of estimation problems: filtering, prediction and smoothing. Filtering aims at the determination of current parameter values, while smoothing and prediction aim at the determination of respectively past and future parameter values. The emphasis in this book will be on recursive least-squares filtering. The theory is worked out for the important case of linear(ized) models. The measurement-update and time-update equations of recursive least-squares are discussed in detail. Models with sequentially collected data, but time-invariant parameters are treated first. In this case only the measurement-update equations apply. State-space models for dynamic systems are discussed so as to include time-varying parameters. This includes their linearization and the construction of the state transition matrix. Elements from the theory of random fun
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Scientific Computing for Chemists with Python
Charles Weiss · Charles J. Weiss · 2025
This book serves as an introduction to coding for chemists. The tools employed in this book are the powerful and popular combination of Jupyter notebooks and the Python programming language. No background beyond first-year college chemistry and occasionally some very basic spectroscopy (for advanced chapters) is assumed for most of this book. This book starts with a brief primer on Jupyter notebooks in chapter 0 and computer programming with Python in chapters 1 and 2. If you already have a background in these tools, feel free to skip ahead. The rest of the book dives into applications of Python to solving chemical problems. Python and Jupyter were chosen for a variety of reasons, including that they are: Relatively easy to use and learn Powerful and well-suited for solving chemical problems Free, open-source software Cross-platform (e.g., runs on Windows, macOS, and Linux) Supplemented with numerous, specialized libraries for handling specific types of data or problems (e.g., machine learning) Supported by a helpful and welcoming community Learning to use a number of popular Python scientific libraries to solve chemical problems is one of the themes of this book. A Python library can be thought of as a tool pack with premade functions for performing common tasks in scientific data processing, analysis, and visualization. For example, the matplotlib library provides a variety of functions for creating a wide range of plots, while the scikit-learn library contains functions and resources for machine learning.