Matematikprogrammer og statistikprogrammer

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.

This book provides the essential theoretical tools for stochastic modeling. The authors address the most used models in applications such as Markov chains with discrete-time parameters, hidden Markov chains, Poisson processes, and birth and death processes. This book also presents specific examples with simulation methods that apply the topics to different areas of knowledge. These examples include practical applications, such as modeling the COVID-19 pandemic and animal movement modeling. This book is concise and rigorous, presenting the material in an easily accessible manner that allows readers to learn how to address and solve problems of a stochastic nature.

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cyber manufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning.Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included.A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions,and supervised and unsupervised learning. These texts can be used independently or for consecutive courses.The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics/."This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications."Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)

Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors¿ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research. Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.

This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods. It covers robust and non-robust descriptive statistics used in each of four bivariate statistical models that are commonly used in research: ANOVA, proportions, regression, and logistic. The text teaches statistical inference principles using resampling methods (such as randomization and bootstrapping), covering methods for hypothesis testing and parameter estimation. These methods are applied to each statistical model introduced in preceding chapters.Data analytic examples are used to teach statistical concepts throughout, and students are introduced to the R packages and functions required for basic data analysis in each of the four models. The text also includes introductory guidance to the fundamentals of data wrangling, as well as examples of write-ups so that students can learn how to communicate findings. Each chapter includes problems forpractice or assessment. Supplemental instructional videos are also available as an additional aid to instructors, or as a general resource to students. This book is intended for an introductory or basic statistics course with an applied focus, or an introductory analytics course, at the undergraduate level in a two-year or four-year institution. This can be used for students with a variety of disciplinary backgrounds, from business, to the social sciences, to medicine. No sophisticated mathematical background is required.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded third edition of Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. Using JMP 17, this book discusses the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison time series forecasting With a new, expansive chapter on time series forecasting and more exercises to test your skills, this third edition is invaluable to those who need to expand their knowledge of statistics and apply real-world, problem-solving analysis.

Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks. In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place. Through case studies in both Python and R, you'll learn to: Obtain NFL data from Python and R packages and web scraping Visualize and explore data Apply regression models to play-by-play data Extend regression models to classification problems in football Apply data science to sports betting with individual player props Understand player athletic attributes using multivariate statistics

This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R.The book also covers flow control and conditional calculations by using ¿¿if¿¿ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them.

Enter the captivating world of Mathematics and Computing with "Introduction to Mathematics for Computing: Algorithms and Data Structures." This comprehensive guide is designed for non-technical enthusiasts, providing an accessible and engaging introduction to essential mathematical concepts for computing.Dive into six insightful chapters that introduce you to the foundations of mathematical structures in computing, discrete mathematics and algorithms, linear algebra and calculus, probability and statistics, optimisation, and Boolean algebra. Explore sets, sequences, functions, graphs, counting principles, and more. Learn about data structures, algorithms, and optimisation techniques used in computing.The book's practice questions, exercises, and projects reinforce the concepts learned, ensuring a solid understanding of these essential topics. Written in accessible and straightforward language, "Introduction to Mathematics for Computing: Algorithms and Data Structures" is the perfect resource for anyone eager to explore the exciting world of Mathematics and Computing. Start your journey today!

This book provides a reference for people working in the design, development, and manufacturing of medical devices. ¿While there are no statistical methods specifically intended for medical devices, there are methods that are commonly applied to various problems in the design, manufacturing, and quality control of medical devices. The aim of this book is not to turn everyone working in the medical device industries into mathematical statisticians; rather, the goal is to provide some help in thinking statistically, and knowing where to go to answer some fundamental questions, such as justifying a method used to qualify/validate equipment, or what information is necessary to support the choice of sample sizes.While, there are no statistical methods specifically designed for analysis of medical device data, there are some methods that seem to appear regularly in relation to medical devices. For example, the assessment of receiver operating characteristic curves is fundamental to development of diagnostic tests, and accelerated life testing is often critical for assessing the shelf life of medical device products. Another example is sensitivity/specificity computations are necessary for in-vitro diagnostics, and Taguchi methods can be very useful for designing devices. Even notions of equivalence and noninferiority have different interpretations in the medical device field compared to pharmacokinetics. It contains topics such as dynamic modeling, machine learning methods, equivalence testing, and experimental design, for example.This book is for those with no statistical experience, as well as those with statistical knowledgeable¿with the hope to provide some insight into what methods are likely to help provide rationale for choices relating to data gathering and analysis activities for medical devices.

The purpose of this book is to thoroughly prepare diverse areas of researchers in quantification theory. As is well known, quantification theory has attracted the attention of a countless number of researchers, some mathematically oriented and others not, but all of them are experts in their own disciplines. Quantifying non-quantitative (qualitative) data requires a variety of mathematical and statistical strategies, some of which are quite complicated. Unlike many books on quantification theory, the current book places more emphasis on preliminary requisites of mathematical tools than on details of quantification theory. As such, the book is primarily intended for readers whose specialty is outside mathematical sciences. The book was designed to offer non-mathematicians a variety of mathematical tools used in quantification theory in simple terms. Once all the preliminaries are fully discussed, quantification theory is then introduced in the last section as a simple application of those mathematical procedures fully discussed so far. The book opens up further frontiers of quantification theory as simple applications of basic mathematics.

This edited book is the first one written in English that deals comprehensively with behavior metrics. The term ¿behaviormetrics¿ comprehends the research including all sorts of quantitative approaches to disclose human behavior. Researchers in behavior metrics have developed, extended, and improved methods such as multivariate statistical analysis, survey methods, cluster analysis, machine learning, multidimensional scaling, corresponding analysis or quantification theory, network analysis, clustering, factor analysis, test theory, and related factors. In the spirit of behavior metrics, researchers applied these methods to data obtained by surveys, experiments, or websites from a diverse range of fields. The purpose of this book is twofold. One is to represent studies that display how the basic elements of behavior metrics have developed into present-day behavior metrics. The other is to represent studies performed mainly by those who would like to pioneer new fieldsof behavior metrics and studies that display elements of future behavior metrics. These studies consist of various characteristics such as those dealing with theoretical or conceptual subjects, the algorithm, the model, the method, and the application to a wide variety of fields. This book helps readers to understand the present and future of behavior metrics.

Ce didacticiel explique les bases de l'utilisation du programme MuPAD et donne un aperçu de la puissance du système. Les principales caractéristiques et les outils de base en sont présentés au cours d'étapes simples. Beaucoup d'exemples et d'exercices illustrent comment utiliser les fonctions, les méthodes graphiques, et le langage de programmation du système. Ce didactciel se rapporte aux versions 1.4, 2.0 ou ultérieures des MuPAD.

Maple is a computer algebraic system with a fast-growing number of users in schools, universities, and other institutions. Intended for anyone who is encountering Maple for the first time, First Steps in Maple provides a detailed step-by-step introduction, teaching by way of examples taken from many areas of mathematics. It is particularly suitable for undergraduates, and has self-test problems (with solutions) for each chapter.

This book is a self-guided tour of MATLAB for engineers and life scientists. It introduces the most commonly used programming techniques through biologically inspired examples. Although the text is written for undergraduates, graduate students and academics, as well as those in industry, will find value in learning MATLAB.The book takes the emphasis off of learning syntax so that the reader can focus more on algorithmic thinking. Although it is not assumed that the reader has taken differential equations or a linear algebra class, there are short introductions to many of these concepts. Following a short history of computing, the MATLAB environment is introduced. Next, vectors and matrices are discussed, followed by matrix-vector operations. The core programming elements of MATLAB are introduced in three successive chapters on scripts, loops, and conditional logic. The last three chapters outline how to manage the input and output of data, create professional quality graphics and find and use MATLAB toolboxes. Throughout, biomedical and life science examples are used to illustrate MATLAB's capabilities.

This book presents a study of the COVID-19 pandemic using computational social scientific analysis that draws from, and employs, statistics and simulations. Combining approaches in crisis management, risk assessment and mathematical modelling, the work also draws from the philosophy of sacrifice and futurology. It makes an original contribution to the important issue of the stability of society by highlighting two significant factors: the COVID-19 crisis as a catalyst for change and the rise of AI and Big Data in managing society. It also emphasizes the nature and importance of sacrifices and the role of politics in the distribution of sacrifices. The book considers the treatment of AI and Big Data and their use to both ¿good¿ and ¿bad¿ ends, exposing the inevitability of these tools being used. Relevant to both policymakers and social scientists interested in the influence of AI and Big Data on the structure of society, the book re-evaluates the ways we think of lifestyles, economic systems and the balance of power in tandem with digital transformation.

The 49Th Scientific meeting of the Italian Statistical Society was held in June 2018 in Palermo, with more than 450 attendants. There were plenary sessions as well as specialized and solicited and contributed sessions.This volume collects a selection of twenty extended contributions covering a wide area of applied and theoretical issues, according to the modern trends in statistical sciences. Only to mention some topics, there are papers on modern textual analysis, sensorial analysis, social inequalities, themes on demography, modern modeling of functional data and high dimensional data, and many other topics.This volume is addressed to academics, PhD students, professionals and researchers in applied and theoretical statistical models for data analysis.

"The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. The book focuses on practical applications rather than dense mathematical concepts, with coding examples using social network data, text data, medical data, and education data"--

Challenge your brain with math! Using nothing more than basic arithmetic and logic, you'll be thrilled as answers slot into place. Whether purely for fun or to test your knowledge, you'll sharpen your problem-solving skills and flex your mental muscles. All you need is logical thought, a little patience, and a clear mind. There are no gotchas here. These puzzles are the perfect introduction to or refresher for math concepts you may have only just learned or long since forgotten. Get ready to have more fun with numbers than you've ever had before.Engage your analytical side with these numerical brain teasers. Math and logic puzzles help you stretch your mind to think in new ways. They flex your lateral thinking as you work through fresh problem styles. Each puzzle type comes with an explanation, a method for solving them, and solutions if you get stuck.The puzzles in this book are short, self-contained, and "gritty." They offer an enjoyable challenge and are designed to be solvable within a few minutes. You only need basic arithmetic to solve these puzzles; no advanced math required. There's plenty of variety to keep things fresh. From wandering digits to magic triangles, from summing grids to water pails, you'll find something that catches your interest. Each puzzle is brief, so use them as a warm-up to your daily work, for a delightful diversion on your coffee break, or solve a few while you wind down for the day.Grab a pencil and your thinking cap, and get solving!

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickhams package development philosophy. In the process, youll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. Youll learn to focus on what you want your package to do, rather than think about package structure.Learn about the most useful components of an R package, including vignettes and unit testsAutomate anything you can, taking advantage of the years of development experience embodied in devtoolsGet tips on good style, such as organizing functions into filesStreamline your development process with devtoolsLearn the best way to submit your package to the Comprehensive R Archive Network (CRAN)Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr