Mathematical and statistical software

Statistical methods are a key tool for all scientists working with data, but learning the basic mathematical skills can be one of the most challenging components of a biologist's training.

Features a simplified presentation of numerical methods by introducing and implementing SAGE programs An Introduction to SAGE Programming: With Applications to SAGE Interacts for Numerical Methods emphasizes how to implement numerical methods using SAGE Math and SAGE Interacts and also addresses the fundamentals of computer programming, including if statements, loops, functions, and interacts.

Systems of polynomial equations arise throughout mathematics, science, and engineering.

This book presents recent non-asymptotic results for approximations in multivariate statistical analysis.

Computers play an increasingly important role in many of today's activities, and correspondingly physicists find employment after graduation in computer- related jobs, often quite remote from their physics education.

This textbook introduces the fundamental concepts and methods of statistical learning.

Starting from a basic knowledge of mathematics and mechanics gained in standard foundation classes, Theory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave takes the reader conceptually through from the fundamental mechanics of lift to the stage of actually being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and planform geometries.

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics.

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data.

Problem Solving and Data Analysis Using Minitab A clear and easy guide to six Sigma methodology Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R.

The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion and Autoregressive models.

The fun and friendly guide to mastering IBM s Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis.

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications.

Learn fromstate-of-the-art examples in robotics, motors, detection filters, chemicalprocesses, aircraft, and spacecraft.

This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain.

This book focuses on recent advances, approaches, theories and applications related to mixture models.

In 2010, the 5th edition of the textbook, "e;Statistics Applied to Clinical Studies"e;, was published by Springer and since then has been widely distributed.

Partial Least Squares (PLS) is a flexible statistical modeling technique that applies to data of any shape.

R, an Open Source software, has become the de facto statistical computing environment.

Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R.

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks.

Get going with tidymodels, a collection of R packages for modeling and machine learning.

Industrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for performing statistical analysis in an industrial context.

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields.

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters.

This book presents a rigorous mathematical development of soil water and contaminant flow in variably saturated and saturated soils.

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

R Visualizations: Derive Meaning from Data focuses on one of the two major topics of data analytics: data visualization, a.

Economists can use computer algebra systems to manipulate symbolic models, derive numerical computations, and analyze empirical relationships among variables.

The fun and friendly guide to mastering IBM s Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis.

Using Stata for Quantitative Analysis offers a brief but thorough introduction to analyzing data in undergraduate and graduate level research methods, statistics, and data analysis courses using Stata software.

Find errors and clean up data easily using SAS!

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.

This book is a result of a workshop, the 8th of the successful TopoInVis workshop series, held in 2019 in Nykoping, Sweden.

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data.

This textbook offers an algorithmic introduction to the field of computer algebra.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book.

Many books teach computational statistics.

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.

This book describes EnvStats, a new comprehensive R package for environmental statistics and the successor to the S-PLUS module EnvironmentalStats for S-PLUS (first released in 1997).

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model.

Construct, analyze, and visualize networks with networkx, a Python language module.