Books in category Computers – Mathematical & Statistical Software

  • Computer Algebra Recipes

    Computer Algebra Recipes
    Richard H. Enns, George C. McGuire

    This text is the first of two volumes. The advanced guide, aimed at junior/senior/graduate level students, deals with more advanced differential equation models.

  • IBM SPSS Statistics 23 Step by Step

    IBM SPSS Statistics 23 Step by Step
    Darren George, Paul Mallery

    Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. All datasets used in the book are available for download at: 9780134320250

  • SAS OR 12 1 User s Guide

    SAS/OR 12.1 User’s Guide
    SAS Institute

    The problem statements are reproduced with permission from the book Model Building in Mathematical Programming by H. Paul Williams. This title is also available online.

  • Data Analysis with Mplus

    Data Analysis with Mplus
    Christian Geiser

    A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts.

  • Data Points

    Data Points
    Nathan Yau

    Reveal the story your data has to tell To create effective data visualizations, you must be part statistician, part designer, and part storyteller.

  • Linear Algebra with Maple Lab Manual

    Linear Algebra with Maple, Lab Manual
    Fred Szabo

    All students majoring in mathematics, computer science, engineering, physics, chemistry, economics, statistics, actuarial mathematics and other such fields of study will benefit from this text.

  • SAS for Mixed Models Second Edition

    SAS for Mixed Models, Second Edition
    Ramon C. Littell, Ph.D., Walter W. Stroup, Ph.D., George A. Milliken, Ph.D., Russell D. Wolfinger, Ph.D., Oliver Schabenberger, Ph.D.

    Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered. This book is part of the SAS Press program.

  • Particle Filters for Random Set Models

    Particle Filters for Random Set Models
    Branko Ristic

    This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering.

  • Learning R

    Learning R
    Richard Cotton

    With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.

  • An Introduction to Statistical Learning

    An Introduction to Statistical Learning
    Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

    This book presents some of the most important modeling and prediction techniques, along with relevant applications.

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