Matematikprogrammer og statistikprogrammer

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.

This book illustrates the use of complex statistical models in medical and health sciences. The use of these models by researchers contributes to the understanding of more complex processes and thus advances knowledge. The various chapters describe how an ordinal logistic model is applied to disabilities, a model in structural equations to an emergency code, multi-state models for the analysis of time-to-event data and Copula models to agitation-sedation scores, spatial models to epidemiology, and statistical methods to clinical trials and to N-of-1 trial design.

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.

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"--

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results

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

Practical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.

Reale Sachverhalte statistisch zu erschließen und zu analysieren ist eine hohe Kunst. Das Programmpaket SPSS ist dafür ein mächtiges Werkzeug. In diesem Buch lernen Sie anhand zahlreicher Beispiele, welche statistischen Verfahren es überhaupt gibt und wann welches Verfahren angemessen ist. Gleich im Anschluss erfahren Sie, wie diese Verfahren in SPSS implementiert sind und wie Sie sie in Ihrem Fachgebiet nutzen können. So finden Sie Zusammenhänge in Ihren Daten, die statistisch signifikant sind.

Learn window function foundational concepts through a cookbook-style approach, beginning with an introduction to the OVER() clause, its various configurations in terms of how partitions and window frames are created, and how data is sorted in the partition so that the window function can operate on the partition data sets. You will build a toolkit based not only on the window functions but also on the performance tuning tools, use of Microsoft Excel to graph results, and future tools you can learn such as PowerBI, SSIS, and SSAS to enhance your data architecture skills.This book goes beyond just showing how each function works. It presents four unique use-case scenarios (sales, financial, engineering, and inventory control) related to statistical analysis, data analysis, and BI. Each section is covered in three chapters, one chapter for each of the window aggregate, ranking, and analytical function categories.Each chapter includes several TSQL code examples and is re-enforced with graphic output plus Microsoft Excel graphs created from the query output. SQL Server estimated query plans are generated and described so you can see how SQL Server processes the query. These together with IO, TIME, and PROFILE statistics are used to performance tune the query. You will know how to use indexes and when not to use indexes.You will learn how to use techniques such as creating report tables, memory enhanced tables, and creating clustered indexes to enhance performance. And you will wrap up your learning with suggested steps related to business intelligence and its relevance to other Microsoft Tools such as Power BI and Analysis Services.All code examples, including code to create and load each of the databases, are available online.What You Will LearnUse SQL Server window functions in the context of statistical and data analysisRe-purpose code so it can be modified for your unique applicationsStudy use-case scenarios that span four critical industriesGet started with statistical data analysis and data mining using TSQL queries to dive deep into dataStudy discussions on statistics, how to use SSMS, SSAS, performance tuning, and TSQL queries using the OVER() clause.Follow prescriptive guidance on good coding standards to improve code legibility Who This Book Is ForIntermediate to advanced SQL Server developers and data architects. Technical and savvy business analysts who need to apply sophisticated data analysis for their business users and clients will also benefit. This book offers critical tools and analysis techniques they can apply to their daily job in the disciplines of data mining, data engineering, and business intelligence.

The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems oriented point of view of biological phenomena.This, the 13th Transactions on Computational Systems Biology volume, guest edited by Ralph-Johan Back, Ion Petre, and Erik de Vink, focuses on Computational Models for Cell Processes and features a number of carefully selected and enhanced contributions initially presented at the CompMod workshop, which took place in Eindhoven, The Netherlands, in November 2009. From different points of view and following various approaches, the papers cover a wide range of topics in systems biology, addressing the dynamics and the computational principles of this emerging field.