Mathematical and statistical software

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This book is about the role and potential of using digital technology in designing teaching and learning tasks in the mathematics classroom.

Multiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python.

This comprehensive resource provides on-the-job training for statistical programmers who use SAS in the pharmaceutical industryThis one-stop resource offers a complete review of what entry- to intermediate-level statistical programmers need to know in order to help with the analysis and reporting of clinical trial data in the pharmaceutical industry.

This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment.

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS.

This book presents essays that address fundamental issues in social and cultural theory by viewing them through the lens of aesthetic theory.

Dieses Buch richtet sich an Studierende verschiedener Fachrichtungen, die das Softwarepaket Octave als kostenfreien und praktischen Lernassistenten nutzen möchten.

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 to marketing to astrophysics in the past twenty years.

This textbook, now in its second edition, provides a broad introduction to the theory and practice of both continuous and discrete dynamical systems with the aid of the Mathematica software suite.

Sir Isaac Newton, one of the greatest scientists and mathematicians of all time, introduced the notion of a vector to define the existence of gravitational forces, the motion of the planets around the sun, and the motion of the moon around the earth.

This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data.

This book discusses the latest advances in algorithms for symbolic summation, factorization, symbolic-numeric linear algebra and linear functional equations.

This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio.

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.

A practical, professional guide to MATLAB computational techniques and engineering applicationsMATLAB Numerical Methods with Chemical Engineering Applications shows you, step by step, how to use MATLAB to model and simulate physical problems in the chemical engineering realm.

Review of the First Edition:The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc.

A guide for MATLAB programmers that offers a collection of standards and guidelines for creating MATLAB code that will be easy to understand.

This is the first numerical analysis text to use Sage for the implementation of algorithms and can be used in a one-semester course for undergraduates in mathematics, math education, computer science/information technology, engineering, and physical sciences.

The third edition of the Student Study Guide With IBM(R) SPSS(R) Workbook for Research Methods, Statistics, and Applications by Kathrynn A.

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Das Buch richtet sich an diejenigen, die Statistik in wirtschaftswissenschaftlich orientierten Studiengängen studieren.

The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand.

This book covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing).

Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment.

The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models.

This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions.

Four years have passed since the first edition of this book.

This book will provide scientists with a better understanding of statistics, improving their decision-making and reducing animal use.

This volume presents the latest advances and trends in nonparametric statistics, and gathers selected and peer-reviewed contributions from the 3rd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Avignon, France on June 11-16, 2016.

Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data.

Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results.

This book is a product of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) to be held in Langkawi in November 2017.

This textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software.

Antony Unwin, Chun-houh Chen, Wolfgang K.

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups.

The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications.

Discover the power of mixed models with SAS.

Little known to many, R works just as well with JavaScript-this book delves into the various ways both languages can work together.

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Mathematica – kurz und bündig ist eine kompakte Einführung in das in der Mathematik sowie in den Natur- und Ingenieurwissenschaften weitverbreitete Programm Mathematica.

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software.

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general.

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research.