Contributors:
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
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.
Contributors:
Ruiz-Sánchez, Herrera-Feijoo, and Correa-Salgado
Publisher:
Editorial Grupo AEA
License:
CC BY-NC-SA
Fundamentos teóricos de química orgánica es una obra integral que proporciona una comprensión sólida de los principios fundamentales de la química orgánica, dirigida tanto a estudiantes como a profesionales. El libro abarca desde los conceptos básicos de la química del carbono hasta la complejidad de las biomoléculas, estructurando el contenido de manera lógica y progresiva. El Capítulo I explora las propiedades de los compuestos orgánicos, los elementos básicos y las formas de representación; seguido del Capítulo II que profundiza en los enlaces covalentes, la hibridación del carbono y las estructuras moleculares. El Capítulo III detalla los grupos funcionales y las reacciones comunes, como adición, eliminación, sustitución y oxidación, mientras que el Capítulo IV aborda la isomería estructural y estereoisomería. Posteriormente, los Capítulos V, VI y VII describen alcanos, alquenos y alquinos, respectivamente, cubriendo definición, formulación, nomenclatura, propiedades y métodos de obtención. El Capítulo VIII trata los hidrocarburos cíclicos y el Capítulo IX los hidrocarburos aromáticos, como el benceno. Además, el Capítulo X se enfoca en halogenuros de alquilo, mientras que los Capítulos XI a XV abordan alcoholes, fenoles, tioles, éteres, aldehídos y cetonas, detallando su definición, formulación, propiedades, obtención y nomenclatura. El Capítulo XVI y el Capítulo XVII discuten ácidos carboxílicos y ésteres, respectivamente, y los Capítulos XVIII a XX cubren aminas, amidas y nitrilos. Finalmente, el Capítulo XXI introduce polímeros naturales y sintéticos, y el Capítulo XXII aborda bioelementos y biomoléculas esenciales para la vida, como carbohidratos, lípidos, proteínas y ácidos nucleicos, proporcionando una base teórica robusta complementada con ejemplos prácticos y aplicaciones, convirtiéndose en una referencia esencial para cualquier persona interesada en la química orgánica.
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.
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.
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.
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.
New to statistics software? This guide is designed just for you. Whether you are studying psychology or any subject that involves analysing data, this easy-to-follow introduction to JASP will help you get to grips with the basics—no previous experience needed. Originally written for first-year psychology students at the University of Sheffield, it walks you through everything from entering data and adjusting settings to running t-tests, correlations, and chi-square analyses. With clear explanations, practical tips, and helpful screenshots, this guide makes learning JASP simple and stress-free.
Mathematical Reasoning and Investigation is designed to help you develop the ability to use mathematics to solve the kinds of problems that don't come with answers in the back of the book. We like to think of it as a mathematics book for people who think they're not good at mathematics. The work will be useful for anyone wanting to develop their own skills in reasoning and problem solving using mathematics, and for teachers and preservice teachers hoping to help their students to develop these same skills.
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