This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.

NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.

Recipient of a 2021 Most Promising New Textbook Award from the Textbook & Academic Authors Association (TAA) "Statistics with R is easily the most accessible and almost fun introduction to statistics and R that I have read. Even the most hesitant student is likely to embrace the material with this text." —David A.M. Peterson, Department of Political Science, Iowa State University Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the "R Team" characters. Inspired by the programming group "R Ladies," the R Team works together to master the skills of statistical analysis and data visualization to untangle real-world, messy data using R. The storylines draw students into investigating contemporary issues such as marijuana legalization, voter registration, and the opioid epidemic, and lead them step-by-step through full-color illustrations of R statistics and interactive exercises. Included with this title: The password-protected Instructor Resource Site (formally known as SAGE Edge) offers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides.

In our digital era, harnessing innovations and emerging technologies to support teaching and learning has been an important research area in the field of education around the world. In science/STEM education, technologies can be leveraged to present and visualize scientific theories and concepts effectively, while the development of pedagogic innovations usually requires collective, inter-disciplinary research efforts. In addition, emerging technologies can better support teachers to assess students’ learning performance in STEM subjects and offer students viable virtual environments to facilitate laboratory-based learning, thereby contributing to sustainable development in both K-12 and higher education.

This two-volume set LNAI 13355 and 13356 constitutes the refereed proceedings of the 23rd International Conference on Artificial Intelligence in Education, AIED 2022, held in Durham, UK, in July 2022. The 40 full papers and 40 short papers presented together with 2 keynotes, 6 industry papers, 12 DC papers, 6 Workshop papers, 10 Practitioner papers, 97 Posters and Late-Breaking Results were carefully reviewed and selected from 243 submissions. The conference presents topics such as intelligent systems and the cognitive sciences for the improvement and advancement of education, the science and engineering of intelligent interactive learning systems. The theme for the AIED 2022 conference was „AI in Education: Bridging the gap between academia, business, and non-pro t in preparing future-proof generations towards ubiquitous AI."

This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail. The book contains scanning data in tropical forests; its indicators, assessment, modeling and implementation. Additionally, debris flow modeling and analysis including source of debris flow identification and rockfall hazard assessment are also presented.

The book discusses the latest developments and outlines future trends in the fields of microelectronics, electromagnetics and telecommunication. It contains original research works presented at the International Conference on Microelectronics, Electromagnetics and Telecommunication (ICMEET 2022), held in Bheemavaram, West Godavari (Dist), Andhra Pradesh, India during 22 – 23 July 2022. The papers were written by scientists, research scholars and practitioners from leading universities, engineering colleges and R&D institutes from all over the world, and share the latest breakthroughs in and promising solutions to the most important issues facing today’s society.

This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2016, held in Stirling, UK, in September 2016. The 19 revised full papers and 6 keynotes abstracts presented were carefully reviewed and selected from 61 submissions. The papers deal with the application of computational intelligence to open problems in bioinformatics, biostatistics, systems and synthetic biology, medicalinformatics, computational approaches to life sciences in general