Digital library
Textbooks, references and open learning resources.
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Introduction to Modern Statistics
Mine Çetinkaya-Rundel; Jo Hardin · OpenIntro · 2024
Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) 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. The second edition of IMS has updated datasets, additional exercises, a new application for chapter 3, and updated text and code to reflect changes in best practices. You can access the full PDF by clicking the Read Free Sample button at the PDF link.
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Yet Another Calculus Text
Dan Sloughter · Dan Sloughter · 2007
I intend this book to be, firstly, a introduction to calculus based on the hyperrealnumber system. In other words, I will use infinitesimal and infinite numbers freely. Just as most beginning calculus books provide no logical justification for the real number system, I will provide none for the hyperreals. The reader interested in questions of foundations should consult books such asAbraham Robinson's Non-standard Analysis or Robert Goldblatt's Lectures onthe Hyperreals. Secondly, I have aimed the text primarily at readers who already have somefamiliarity with calculus. Although the book does not explicitly assume any prerequisites beyond basic algebra and trigonometry, in practice the pace istoo fast for most of those without some acquaintance with the basic notions of calculus.
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Applied Probability
Paul Pfeiffer · OpenStax CNX · 2009
This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired. In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks. Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals). Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.
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Linear Algebra, Theory And Applications
Kenneth Kuttler · Saylor Foundation · 2012
This is a book on linear algebra and matrix theory. While it is self contained, it will work best for those who have already had some exposure to linear algebra. It is also assumed that the reader has had calculus. Some optional topics require more analysis than this, however. This book features an ugly, elementary, and complete treatment of determinants early in the book. Thus it might be considered as Linear algebra done wrong. I have done this because of the usefulness of determinants. However, all major topics are also presented in an alternative manner which is independent of determinants. The book has an introduction to various numerical methods used in linear algebra. This is done because of the interesting nature of these methods. The presentation here emphasizes the reasons why they work. It does not discuss many important numerical considerations necessary to use the methods effectively. These considerations are found in numerical analysis texts.
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Slavery to Liberation: The African American Experience
Joshua Farrington; Norman Powell; Gwendolyn Graham; Ogechi Anyanwu · Encompass Digital Archive · 2019
Slavery to Liberation: The African American Experience gives instructors, students, and general readers a comprehensive and up-to-date account of African Americans’ cultural and political history, economic development, artistic expressiveness, and religious and philosophical worldviews in a critical framework. It offers sound interdisciplinary analysis of selected historical and contemporary issues surrounding the origins and manifestations of White supremacy in the United States. By placing race at the center of the work, the book offers significant lessons for understanding the institutional marginalization of Blacks in contemporary America and their historical resistance and perseverance.
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Reading Social Science Methods
Ann Reisner · University of Illinois Library - Urbana · 2023
Science has great potential to benefit society, but this potential comes with risks as well. Directed at introductory level social science and humanities majors, this textbook teaches the rules and limits of social science methods. Reisner starts from the assumption that it is not necessary to be able to do research to read and judge the soundness of research publications. The chapters guide students through an explicit set of rules for reading research articles developed from three common research methods: content analysis, survey research, and experimental method.
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Introduction to Systems Biology: Workbook for Flipped-Classroom Teaching
Thomas Sauter; Marco Albrecht · Open Book Publishers · 2023
This book is an introduction to the language of systems biology, which is spoken among many disciplines, from biology to engineering. Authors Thomas Sauter and Marco Albrecht draw on a multidisciplinary background and evidence-based learning to facilitate the understanding of biochemical networks, metabolic modeling and system dynamics. Their pedagogic approach briefly highlights core ideas of concepts in a broader interdisciplinary framework to guide a more effective deep dive thereafter. The learning journey starts with the purity of mathematical concepts, reveals its power to connect biological entities in structure and time, and finally introduces physics concepts to tightly align abstraction with reality. This workbook is all about self-paced learning, supports the flipped-classroom concept, and kick-starts with scientific evidence on studying. Each chapter comes with links to external YouTube videos, learning checklists, and Integrated real-world examples to gain confidence in thinking across scientific perspectives. The result is an integrated approach that opens a line of communication between theory and application, enabling readers to actively learn as they read. This overview of capturing and analyzing the behavior of biological systems will interest adherers of systems biology and network analysis, as well as related fields such as bioinformatics, biology, cybernetics, and data science.
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Wellbeing, Freedom and Social Justice: The Capability Approach Re-Examined (Robeyns)
Ingrid Robeyns · Utrecht University
In the contents: 2.04: The many modes of capability analysis
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CHM 1020: General Chemistry II
William Miller · Prince George's Community College
In the contents: 4.07: Qualitative Chemical Analysis
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MSU: CHMY311 Fundamental Analytical Chemistry
Jane Klassen · Montana State University
In the contents: 3.01: Analysis Determination and Measurement · 4.05: Statistical Analysis of Data · 06: Obtaining and Preparing Samples for Analysis
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Math 27: Number Systems for Educators
Ashley McHale · Las Positas College
This text focuses on the development of quantitative reasoning skills through in-depth, integrated explorations of topics in mathematics, including real number systems and subsystems. Emphasis is on comprehension and analysis of mathematical concepts and applications of logical reasoning. (Photo Credit: by Joshua Eckstein on Unsplash)
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B-CU: CH-141 General Chemistry 1
Sidney Bolden Jr. · Bethune-Cookman University
In the contents: 7.5: Quantitative Chemical Analysis
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Social Media for Public Relations
Mark Poepsel & Sarah VanSlette · Southern Illinois University Edwardsville
In the contents: 5.01: Client Selection Competitive Analysis and SWOT Analysis
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Abnormal Psychology
Lumen Learning · Lumen Learning
In the contents: 05: Self Analysis (or pseudo self-analysis)
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College Mathematics - Abridged Edition
Cayla M Gaworecki · SUNY Schenectady County Community College
In the contents: 01: Measurement and Dimensional Analysis .. · 1.05: Problem Solving Using Rates and Dimensional Analysis .
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Chemistry 101A General College Chemistry
Torrey Glenn · City College of San Francisco
In the contents: 1.1.05: Empirical Formulas from Analysis · 2.1.07: Solution Stoichiometry and Chemical Analysis
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Physical Science for Educators Volume 1
Timothy Evans, Kylee Ford, Kenneth Henry, Yogita Kumari, Claude Mona & Suzanne Wakim · Coalinga College
In the contents: 2.2.09: Percents Part 2 and Error Analysis · 2.8.09: Dimensional Analysis
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CCSF Chemistry Resources
LibreTexts
In the contents: 2.0.02: Dimensional Analysis · 2.0.03: Dimensional Analysis (Problems)
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MTH 60: Algebra 1
Math Department · Clackamas Community College
In the contents: 1.06: Dimensional Analysis
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Chemometrics Using R
David Harvey · DePauw University
In the contents: 7.03: Analysis of Variance · 7.05: Using R for Significance Testing and Analysis of Variance · 8.05: Using R for a Linear Regression Analysis