MGF 1131: Mathematics in Context
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Author: Godwin
Publisher: LibreTexts
License: CC BY-NC-SA
Textbook used by students in MGF 1131 at Northwest Florida State College.
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Discrete Math with SageMath: Learn math with open-source software
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Authors: Ahmed, Colman, and Lubliner
Publisher: Zunaid Ahmed, Hellen Colman, Samuel Lubliner
License: CC BY
Our book is intended to provide concise and complete instructions on how to use Sage to solve problems in Discrete Math. It is appropriate for a first or second year undergraduate course for math and computer science majors. Our goal is to streamline the learning process with SageMath. This approach helps students focus more on mathematics and reduces the friction of learning how to code. Our resources are designed for all math students, regardless of programming experience.
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Learning Statistics with jamovi: A Tutorial for Beginners in Statistical Analysis
Copyright Year:
Authors: Navarro and Foxcroft
Publisher: Open Book Publishers
License: CC BY-SA
Based on Danielle Navarro’s widely acclaimed and prize-winning book Learning Statistics with R, this elegantly designed textbook offers undergraduate students a thorough and accessible introduction to jamovi, as well as how to get to grips with statistics and data manipulation. Lucid and easy to understand, Learning Statistics with jamovi covers the analysis of contingency tables, t-tests, correlation, regression, ANOVA and factor analysis, while also giving students a firm grounding in descriptive statistics and graphing. It includes learning aids for applying statistical principles using the jamovi interface, as well as embedded data files to accompany the book, and comprehensive chapters on probability theory, sampling and estimation, and null hypothesis testing. Freely available in open access, Learning Statistics with jamovi is an ideal introduction for undergraduate and postgraduate students of psychology, as well as behavioural and health science students and anyone who needs to understand and use statistical analysis in their work.
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Linear Algebra Done Right - Fourth Edition
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Author: Axler
Publisher: Springer
License: CC BY-NC
Now available in Open Access, this best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. The fourth edition gives an expanded treatment of the singular value decomposition and its consequences. It includes a new chapter on multilinear algebra, treating bilinear forms, quadratic forms, tensor products, and an approach to determinants via alternating multilinear forms. This new edition also increases the use of the minimal polynomial to provide cleaner proofs of multiple results. Also, over 250 new exercises have been added.
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Statistics Minus The Math
Copyright Year:
Author: Favero
Publisher: Nathan Favero
License: CC BY
This book was largely adapted from the public domain resource Online Statistics Education: A Multimedia Course of Study (https://onlinestatbook.com Project Leader: David M. Lane, Rice University). A huge thanks to David Lane and his colleagues at Rice University for their creation of this wonderful resource. I use footnotes throughout to indicate precisely where the various sections of each chapter came from. Chapters 11-13 (as well as Section 3.5, Section 4.2.2, most of Section 4.2.3, and Section 9.3.1) were written by me (Nathan). This book is meant to be a free resource and is licensed under CC BY 4.0. You’re welcome to share or adapt it, as long as you provide attribution to any work of mine that you use. This book was made using Quarto and is hosted using GitHub Pages. There are still formatting inconsistencies, and this is ever a work in progress. If you find errors, feel free to reach out (find updated contact info here: https://nathanfavero.com) so I can correct them for the next version I publish.
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The Crystal Ball Instruction Manual Volume Two: Foundations For Data Science
Copyright Year:
Author: Davies
Publisher: University of Mary Washington
License: CC BY-SA
Welcome to Volume Two of the Crystal Ball series: Foundations for Data Science! I titled the first volume “Introduction to Data Science” because it led you through a dip-your-toes-in-the-water experience. You took a brief tour through the various elements in this diverse field and got a feel for what it was all about.
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Testing theory: an introduction
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Author: Teunissen
Publisher: TU Delft Open
License: CC BY
These lecture notes are a follow up on Adjustment theory. Adjustment theory deals with the optimal combination of redundant measurements together with the estimation of unknown parameters. There are two main reasons for performing redundant measurements. First, the wish to increase the accuracy of the results computed. Second, the requirement to be able to check for mistakes or errors. The present book addresses this second topic. Although one always will try one's best to avoid making mistakes, they can and will occasionally happen. It is therefore of importance to have ways of detecting and identifying such mistakes. Mistakes or errors can come in many different guises. They could be caused by mistakes made by the observer, or by the fact that defective instruments are used, or by wrong assumptions about the functional relations between the observables. When passed unnoticed, these errors will deteriorate the final results.
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An Introduction to Python Jupyter Notebooks for College Math Teachers
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Authors: Isihara, Wagner, Jantsch, and VanDrunen
Publisher: Paul Isihara, Claire Wagner, Peter Jantsch, and Thomas VanDrunen
License: CC BY
This book is an introduction to the use of Python Jupyter Notebooks (JNBs) for college math teachers and their students. The book is an evolving work in progress, but we hope this 1st Edition may already be quite useful to a college math teacher who is interested to use Jupyter Notebooks in their courses. Each section of the book has been written in the form of a Jupyter Notebook and can be downloaded using the download button at the top menu bar. In some cases, additional data files or image files may need to be downloaded as well. All the files are available in the src folder of the github repository https://github.com/timothyprojectGiG/JB_Math_Textbook
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Adjustment theory: an introduction
Copyright Year:
Author: Teunissen
Publisher: TU Delft Open
License: CC BY
Adjustment theory can be regarded as the part of mathematical geodesy that deals with the optimal combination of redundant measurements together with the estimation of unknown parameters. It is essential for a geodesist, its meaning comparable to what mechanics means to a civil engineer or a mechanical engineer. Historically, the first methods of combining redundant measurements originate from the study of three problems in geodesy and astronomy, namely to determine the size and shape of the Earth, to explain the long-term inequality in the motions of Jupiter and Saturn, and to find a mathematical representation of the motions of the Moon. Nowadays, the methods of adjustment are used for a much greater variety of geodetic applications, ranging from, for instance, surveying and navigation to remote sensing and global positioning.
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Basic Statistics Using R for Crime Analysis
Copyright Year:
Author: Choi
Publisher: The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT)
License: CC BY-SA
Limited access to subscription-based statistical software poses obstacles when students want to apply the skills they acquired in college. Although students may learn programs like SPSS or Stata while at the university, they often find themselves unable to continue using these programs after graduation, making their acquired skills obsolete. As an open-source software program, R offers a solution to this challenge. It is freely accessible to anyone, including students, after they graduate. Therefore, I decided to write a freely available book for those interested in becoming crime analysts, focusing on learning statistics without delving too deeply into mathematics. Moreover, this book emphasizes practical applications by utilizing R for data analysis, ensuring students can develop relevant skills beyond the university. I hope that students can easily follow the instructions in this book and replicate the same outcomes using the provided data. This practical experience will demonstrate the value of statistics and R, ideally inspiring students to further their learning in these areas.
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