Mathematical and statistical software

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.

PsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics.

PsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data.

Computational approaches offer exciting opportunities for us to do social science differently.

Early training in the elementary techniques of partial differential equations is invaluable to students in engineering and the sciences as well as mathematics.

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource.

Program for data analysis using R and learn practical skills to make your work more efficient.

Want to use the power of R sooner rather than later?

Complex Survey Data Analysis with SAS(R) is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors.

This text is meant to be a hands-on lab manual that can be used in class every day to guide the exploration of the theory and applications of differential and integral calculus.

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets.

This book is intended for someone learning functions of a complex variable and who enjoys using MATLAB.

A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science.

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications, Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations.

Many of the product recalls that we see daily are due to unexpected failures resulting from poor reliability analysis during the product design stage.

Virtually any random process developing chronologically can be viewed as a time series.

Virtually any random process developing chronologically can be viewed as a time series.

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R.

Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice.

Introduction to Number Theory is a classroom-tested, student-friendly text that covers a diverse array of number theory topics, from the ancient Euclidean algorithm for finding the greatest common divisor of two integers to recent developments such as cryptography, the theory of elliptic curves, and the negative solution of Hilbert's tenth problem.

All the Essentials to Start Using Adaptive Designs in No TimeCompared to traditional clinical trial designs, adaptive designs often lead to increased success rates in drug development at reduced costs and time.

Introduction to Number Theory is a classroom-tested, student-friendly text that covers a diverse array of number theory topics, from the ancient Euclidean algorithm for finding the greatest common divisor of two integers to recent developments such as cryptography, the theory of elliptic curves, and the negative solution of Hilbert's tenth problem.

This text provides a modern introduction to regression and classification with an emphasis on big data and R.

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.

This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability.

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling.

This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.

This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data.

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing.

R for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud.

This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for Non Parametric Statistics (ISNPS).

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

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

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

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

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis.

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition.

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

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

Its much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet.

Its much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet.

Post-Optimal Analysis in Linear Semi-Infinite Optimization examines the following topics in regards to linear semi-infinite optimization: modeling uncertainty, qualitative stability analysis, quantitative stability analysis and sensitivity analysis.