Digital library
Textbooks, references and open learning resources.
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Acoustics
Wikipedia · LibreTexts
Acoustics is the science that studies sound, in particular its production, transmission, and effects. The science of acoustics has many applications which are dependent upon the nature of the sound that is to be produced, transmitted or controlled. In the case of a desirable sound, such as music, the main application of acoustics is to make the music sound as good as possible.
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UH Maui College: Psychology 212 Research Methods
Paul Thornton · University of Hawaii Maui College
In the contents: 07: Descriptive Statistics · 7.5: Descriptive Statistics (Summary) · 10: Inferential Statistics
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Statistical Thermodynamics
Gunnar Jeschke · ETH Zürich
In the contents: 5.02: Quantum and Classical Statistics
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Experimental Design
Lydia Yang · Kansas State University
In the contents: 2.03: Computing the F statistics
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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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Foundations in Statistical Reasoning
Peter Kaslik · Pierce College Fort Steilacoom
In the contents: 03: Examining the Evidence using Graphs and Statistics · 3.E: Examining the Evidence using Graphs and Statistics (Exercises)
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Introductory Probability
Charles Grinstead & J. Laurie Snell · Dartmouth & Swarthmore Universities
This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject. The text can be used in a variety of course lengths, levels, and areas of emphasis.
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Mathematical Reasoning
Pamini Thangarajah · Mount Royal University
In the contents: 6: Introduction to Statistics · 6.E: Introduction to Statistics (Exercises)
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Time Series Analysis
Alexander Aue · University of California, Davis
A time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.
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Quantitative Methods for Plant Breeding
Walter Suza and Kendall Lamkey · Iowa State University
This open textbook covers common statistics used in agriculture research, including experimental design in plant breeding and genetics, as well as the analysis of variance, regression, and correlation.
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Analytical Chemistry 2.1
David Harvey · DePauw University
The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum for exploring topics such as experimental design, sampling, calibration strategies, standardization, optimization, statistics, and the validation of experimental results. Analytical methods come and go, but best practices for designing and validating analytical methods are universal.
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Non-Equilibrium Statistical Mechanics
Jianshu Cao · Massechusetts Institute of Technology
This course discusses the principles and methods of non-equilibrium statistical mechanics. Basic topics covered are stochastic processes, regression and response theory, molecular hydrodynamics, and complex liquids. Selected applications, including fluctuation theorems, condensed phase reaction rate theory, electron transfer dynamics, enzymatic networks, photon counting statistics, single molecule kinetics, reaction-controlled diffusion, may also be discussed.
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Research Methods in Psychology
Carrie Cuttler et al. · Washington State University
In the contents: 12: Descriptive Statistics · 13: Inferential Statistics · 13.0: Prelude to Inferential Statistics
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Statistical Thinking for the 21st Century
Russell A. Poldrack · Stanford University
In the contents: 1.02: Dealing with Statistics Anxiety · 1.03: What Can Statistics Do for Us · 1.04: The Big Ideas of Statistics
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ReCentering Psych Stats
Lynette Bikos · Seattle Pacific University Library · 2023
To center a variable in regression means to set its value at zero and interpret all other values in relation to this reference point. Regarding race and gender, researchers often center male and White at zero. Further, it is typical that research vignettes in statistics textbooks are similarly seated in a White, Western (frequently U.S.), heteronormative, framework. ReCentering Psych Stats seeks provide statistics training for psychology students (undergraduate, graduate, and post-doctoral) in a socially and culturally responsive way. All lessons use the open-source statistics program, R (and its associated packages). Each lesson includes a chapter and screencasted lesson, features a workflow for statistical decision-making, and includes all R code necessary to conduct the statistic. Research vignettes are drawn from the published psychology literature. When possible, these articles are authored by individuals who hold identities that have, been marginalized in the scientific literature; correctly use the statistic that is being taught in the lesson; and focus on issues of justice, equity, inclusion, or and diversity. When possible, lessons include interviews with researchers from the featured vignettes. Each chapter includes suggestions for practice that are graded in complexity, such that learners can choose the degree of challenge. ReCentering Psych Stats is perpetually-in-progress; suggestions for corrections or chapters are welcomed: ReCenteringPsychStats@spu.edu
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Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R)
Hank Jenkins-Smith; Joseph Ripberger · University of Oklahoma Libraries · 2017
The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models.The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.
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Introduction to Psychology
University of Minnesota Libraries Publishing · 2015
When you teach Introduction to Psychology, do you find it difficult — much harder than teaching classes in statistics or research methods? Do you easily give a lecture on the sympathetic nervous system, a lecture on Piaget, and a lecture on social cognition, but struggle with linking these topics together for the student? Do you feel like you are presenting a laundry list of research findings rather than an integrated set of principles and knowledge? Have you wondered how to ensure your course is relevant to your students? Introduction to Psychology utilizes the dual theme of behavior and empiricism to make psychology relevant to intro students. The author wrote this book to help students organize their thinking about psychology at a conceptual level. Five or ten years from now, he does not expect his students to remember the details of most of what he teaches them. However, he does hope that they will remember that psychology matters because it helps us understand behavior and that our knowledge of psychology is based on empirical study. This book is designed to facilitate these learning outcomes, and he has used three techniques to help focus students on behavior: Chapter Openers: Each chapter opens showcasing an interesting real world example of people who dealing with behavioral questions and who can use psychology to help them answer them. The opener is designed to draw the student into the chapter and create an interesting in learning about the topic. Psychology in Everyday Life: Each chapter contains one or two features designed to link the principles from the chapter to real-world applications in business, environment, health, law, learning, and other relevant domains. For instance, the application in Chapter 7 on Development, ”What makes good parents“ applies the concepts of parenting styles in a mini-handbook about parenting, and the application in Chapter 3 is about the difficulties that left-handed people face performing everyday tasks in a right-handed
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Quantitative Methods for Plant Breeding
Walter Suza; Kendall Lamkey · Iowa State University Digital Press · 2023
This open textbook covers common statistics used in agriculture research, including experimental design in plant breeding and genetics, as well as the analysis of variance, regression, and correlation.
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Analytical Chemistry 2.1
David Harvey · David T. Harvey · 2016
As currently taught in the United States, introductory courses in analytical chemistry emphasize quantitative (and sometimes qualitative) methods of analysis along with a heavy dose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection of analytical methods and an understanding of equilibrium chemistry; it is an approach to solving chemical problems. Although equilibrium chemistry and analytical methods are important, their coverage should not come at the expense of other equally important topics. The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum for exploring topics such as experimental design, sampling, calibration strategies, standardization, optimization, statistics, and the validation of experimental results. Analytical methods come and go, but best practices for designing and validating analytical methods are universal. Because chemistry is an experimental science it is essential that all chemistry students understand the importance of making good measurements. My goal in preparing this textbook is to find a more appropriate balance between theory and practice, between “classical” and “modern” analytical methods, between analyzing samples and collecting samples and preparing them for analysis, and between analytical methods and data analysis. There is more material here than anyone can cover in one semester; it is my hope that the diversity of topics will meet the needs of different instructors, while, perhaps, suggesting some new topics to cover.
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Natural Resources Biometrics
Diane Kiernan · Open SUNY · 2014
Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.