Digital library
Textbooks, references and open learning resources.
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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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Statistical Thinking for the 21st Century
Russell Poldrack · Russell Poldrack · 2018
Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.
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Integral Calculus with Applications to Life Sciences
Paul Tsopméné · University of British Columbia Library · 2025
Integral Calculus with Applications to Life Sciences is an open educational resource (OER) that covers an unusually broad range of topics—including integration, infinite series, differential equations, linear algebra, probability, and statistics—rarely found in a single textbook. Each section begins with clear learning outcomes and a motivational paragraph, often tied to applications in the life sciences, helping students see the immediate relevance of what they learn. Examples are worked out in full detail. Every time a calculus or algebraic rule is applied, it is explicitly stated at the point of use. Rather than isolating algebra review in a preliminary chapter, this text integrates algebra throughout, allowing students to reinforce foundational skills while learning new material. This approach is especially supportive for students who may struggle with algebra. Practice problems with complete solutions follow each example, and most sections end with an application to biology, medicine, or related fields. The exercise sections include not only standard questions but also critical thinking problems such as finding errors in worked solutions, constructing counterexamples, and solving real-world problems. The text includes final answers for all exercises at the back. Hyperlinks allow students to jump seamlessly between exercises and final answers. By combining clarity, rigor, accessibility, and real-world relevance, one of the main goals of this OER is to help students not only to succeed in calculus but also to appreciate its power in understanding the living world.
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Introduction to Probability
John Baxter · John R. Baxter · 2026
This is an introduction to probability theory, designed for self-study. It covers the same topics as the one-semester introductory courses which I taught at the University of Minnesota, with some extra discussion for reading on your own. The reasons which underlie the rules of probability are emphasized. Probability theory is certainly useful. But how does it feel to study it? Well, like other areas of mathematics, probability theory contains elegant concepts, and it gives you a chance to exercise your ingenuity, which is often fun. But in addition, randomness and probability are part of our experience in the real world, present everywhere and yet still somewhat mysterious. This gives the subject of probability a special interest.
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Polimetrics: A Stata Companion to Introduction to Political Science Research Methods
Josh Franco · Open Political Science (OPoliSci) · 2021
Polimetrics: A Stata Companion, authored by Dr. Josh Franco, is an Open Education Resource workbook licensed CC BY-NC and designed as a Stata companion to Introduction to Political Science Research Methods. This workbook provides a tour of the Stata software, an introduction to cross-sectional, time series, and panel data, and an introduction to a variety of models. I review models where the outcome is linear, binary, ordinal, categorical, and count. Additionally, I have an interpretation chapter on survival models. Each “Models” chapter has a similar organizational structure: about, estimated time, what is the model, how are models run in Stata, how do we interpret the model results, and a real-world example of model results in a Creative Commons licensed, peer-reviewed journal article. Additionally, mini-assignment instructions and a rubric are included so students can practice their interpretation skills.
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Contemporary Mathematics
Donna Kirk · OpenStax · 2023
Contemporary Mathematics is designed to meet the scope and sequence requirements for a liberal arts mathematics course. This resource provides stand-alone sections with a focus on showing relevance in the features as well as the examples, exercises, and exposition. Contemporary Mathematics integrates technology applications, projects, and highlights a diverse group of contributors to mathematics, statistics, and related fields. Print copies of this book will be available for the fall 2023 semester. You can sign up to be notified when print copies are available.
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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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Statistical Inference For Everyone
Brian Blais · Brian Blais · 2017
This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.
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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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Process Improvement using Data
Kevin Dunn · Kevin Dunn · 2026
There is no other free, coherent text that covers what engineers and scientists actually do with process data (visualization, regression, designed experiments, process monitoring, and multivariate / latent-variable methods) in one volume. Most textbooks pick one of those topics and go deep. Practitioners need all of them, and need to see how they fit together, because real industrial problems don't respect chapter boundaries. Process Improvement using Data was written to fill that gap, and has been continuously refined in industry-facing classrooms and in industrial practice since 2010. It is suitable for upper-undergraduate or introductory-graduate courses, and for self-study by working engineers and data scientists with a basic statistics background.
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Chemical Process Dynamics and Controls
Peter Woolf · openmichigan · 2009
Process controls is a mixture between the statistics and engineering discipline that deals with the mechanism, architectures, and algorithms for controlling a process. Some examples of controlled processes are: •Controlling the temperature of a water stream by controlling the amount of steam added to the shell of a heat exchanger. •Operating a jacketed reactor isothermally by controlling the mixture of cold water and steam that flows through the jacket of a jacketed reactor. •Maintaining a set ratio of reactants to be added to a reactor by controlling their flow rates. •Controlling the height of fluid in a tank to ensure that it does not overflow.
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Experimental Design and Technical Writing for Engineers
Susan Ely · University of Southern Indiana · 2024
This text is intended for undergraduate engineering students completing a technical writing course. As engineers complete technical writing in conjunction with research activities, this work also includes a basic overview of experimental design and the implications of robust quantitative methodologies. Hallmarks of quantitative research, best practices in presentation of data and a brief description of the use of descriptive statistics in data analysis are also provided. Technical communications for engineers often include industry related documents like memos, executive summaries, case studies, quality and process documents, standard operating procedures and requests for proposals. These documents are discussed, in addition to formal technical reports. The text also includes best practices in engineering oral presentations and the impact of Artificial Intelligence on technical communications. Ethical implications of engineering communications are also discussed from a framework of professional integrity as defined by the National Society of Professional Engineers Code of Ethics. Students using this text should be capable of communicating, in both verbal and written formats, in a way that clearly, concisely and correctly conveys engineering principles, projects and concepts to both technical and nontechnical audiences.
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Introduction to Data Science Using Python
Afrand Agah · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2024
This book contains two parts, the first is designed to be used in an introductory programming course for students looking to learn Python, without having any prior experience with programming. Basic programming concepts are discussed, explained, and illustrated with a Python program. Ample programming questions are provided for practice. The second part of the book utilizes machine-learning concepts and statistics to accomplish data-driven resolutions. Python programs are provided to apply scientific computing to conclude statistically driven results.
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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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The Discipline of Organizing: 4th Professional Edition
Robert Glushko · University of California, Berkeley · 2013
We organize things, we organize information, we organize information about things, and we organize information about information. But even though “organizing” is a fundamental and ubiquitous challenge, when we compare these activities their contrasts are more apparent than their commonalities. We propose to unify many perspectives about organizing with the concept of an Organizing System, defined as an intentionally arranged collection of resources and the interactions they support. Every Organizing System involves a collection of resources, a choice of properties or principles used to describe and arrange resources, and ways of supporting interactions with resources. By comparing and contrasting how these activities take place in different contexts and domains, we can identify patterns of organizing. We can create a discipline of organizing in a disciplined way. The 4th edition builds a bridge between organizing and data science. It reframes descriptive statistics as organizing techniques, expands the treatment of classification to include computational methods, and incorporates many new examples of data-driven resource selection, organization, maintenance, and personalization. It introduces a new “data science” category of discipline-specific content, both in the chapter text and in endnotes, marked with [DS] in editions that contain endnotes.
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Applied Business Analytics for Marketing Decision-Making
Jose Mendoza · New York University · 2026
Applied Business Analytics for Marketing Decision-Making is an applied textbook designed to help students use data, analytical methods, and visualization to support marketing and business decisions. Written for graduate students in marketing, integrated marketing, business analytics, and related professional programs, the book assumes limited prior experience with programming or statistics and emphasizes conceptual understanding, practical application, and managerial interpretation.
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Gateway to Business Analytics with Microsoft Excel®
Humberto Barreto · PALNI · 2025
Business analytics is a new, expanding subfield with fuzzy edges that overlap into a variety of other established disciplines, including economics, econometrics, computer science, data science, finance, statistics, mathematics, and even psychology. Business analytics includes such traditional techniques as regression and data visualization, but also newer methods such as web scraping, big data, machine learning, and artificial intelligence. Its key distinguishing feature is using data to make and communicate business decisions. This textbook utilizes Microsoft Excel to present a mix of topics appropriate to an undergraduate level introduction to business analytics course with an engaging delivery style.
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The Legal and Ethical Environment of Business
Terence Lau; Lisa Johnson · Saylor Foundation · 2011
Terence Lau & Lisa Johnson's The Legal and Ethical Environment of Business is a book for today's student, who expects learning to be comprised not only of substance, but also of interactive exercises and multimedia. This book streamlines the presentation of material to ensure that every page is relevant, engaging, and interesting to undergraduate business students, without losing the depth of coverage that they need to be successful in their academic journeys and in their professional careers. This is not Legal Environment of Business (LEB) ”light.“ Rather, this is LEB without risk of students' eyes glazing over in boredom or from lack of comprehension. This is LEB presented in an exciting way, where every page is interesting to students and relevant to real life. The authors recognize that the sheer volume of information to be covered in a LEB course makes it one of the more challenging courses for the business undergraduate. Not only do typical LEB texts read like the first year curriculum at law school, but also the LEB course is grounded in the humanities, which can make the subject even more demanding for students who are also taking statistics, economics, finance, and accounting. Each chapter contains not only substantive law, but also illustrative videos, interactive exercises for hands-on learning, and discussion questions for critical thought. Additionally, each chapter presents ”A Question of Ethics“ section, which contains real world ethical dilemmas relevant to the topic under study. These videos, exercises, discussion questions, and ethics sections all provide opportunities for students to apply concepts that they are learning in the context of relevant LEB topics that shape or restrain actual decision-makers' actions. It's real world practice in the safety of the classroom environment. Lau & Johnson's The Legal and Ethical Environment of Business is a textbook that students will enjoy reading. This will encourage them to come to class prepared, and fre