Mathematical and statistical software

A comprehensive manual on the efficient modeling and analysis of photonic devices for graduate students and researchers in engineering and physics.

The second edition of this textbook provides a comprehensive and detailed overview of MATLAB and specific tools for creating experiments and analysing data in psychology.

AMS packages that replaced AMS-Ib-TEX, version 1.

Go from total MATLAB newbie to plotting graphs and solving equations in a flash!

A how to guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation.

IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference, sixteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike.

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

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS Performing Data Analysis Using IBM SPSS uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets.

A hands-on guide to using Excel in the business context First published in 2012, Using Excel for Business and Financial Modelling contains step-by-step instructions of how to solve common business problems using financial models, including downloadable Excel templates, a list of shortcuts and tons of practical tips and techniques you can apply straight away.

This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.

In honor of professor and renowned statistician R.

Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool.

SummaryBeyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more.

Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.

Get started computing with data.

This book provides beginners with a thorough foundation in SAS macro programming.

This book, using R and RStudio, demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.

In recent years developments in statistics have to a great extent gone hand in hand with developments in computing.

S is a high-level language for manipulating, analysing and displayingdata.

A compact introduction to this active and powerful area of research, combining basic theory, core techniques, and recent applications.

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics.

This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables.

If you know how to program, you're ready to tackle Bayesian statistics.

Dynamical system theory has developed rapidly over the past fifty years.

This book provides a tutorial in the use of Altair Compose and Altair Activate, software packages that provide system modeling and simulation facilities.

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables.

A step-by-step guide to predictive modeling!

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

Numerical linear algebra is one of the most important subjects in the field of statistical computing.

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS Performing Data Analysis Using IBM SPSS uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets.

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience.

Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware.

The application and interpretation of statistics are central to ecological study and practice.

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

This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions.

This introductory textbook presents research methods and data analysis tools in non-technical language.

1.

This book illustrates the potential for computer simulation in the study of modern slavery and worker abuse, and by extension in all social issues.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area.

Balanced coverage of the methodology and theory of numerical methods in finance Numerical Methods in Finance bridges the gap between financial theory and computational practice while helping students and practitioners exploit MATLAB for financial applications.

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods.

Learn to program in R from the experts with this new, color edition of Braun and Murdoch''s bestselling textbook.

To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject.

Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets.

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.