Download or read book Statistics with Mathematica written by Martha L. Abell and published by Academic Press. This book was released on 1999 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers the use of Mathematica for applications ranging from descriptive statistics, through multiple regression and nonparametric methods; uses virtually all of Mathematica's built-in statistical commands, as well as those contained in various Mathematica packages; Additionally, the authors have written numerous procedures to extend Mathematica's capabilities, which are also included on the CD-ROM

Download or read book Statistics with Mathematica Text written by Martha L. Abell and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mathematical Statistics with Mathematica written by Colin Rose and published by Springer. This book was released on 2002 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text and software package presents a unified approach for doing mathematical statistics with Mathematica. The mathStatica software empowers the student with the ability to solve difficult problems. The professional statistician should be able to tackle tricky multivariate distributions, generating functions, inversion theorems, symbolic maximum likelihood estimation, unbiased estimation, and the checking and correcting of textbook formulae. This is the ideal companion for researchers and students in statistics, econometrics, engineering, physics, psychometrics, economics, finance, biometrics, and the social sciences. The mathStatica CD-ROM includes: mathStatica - the applications pack for mathematical statistics, custom Mathematica palettes, live interactive book that is identical to the printed text, online help, and a trial version of Mathematica 4.0.

Download or read book Mathematica Laboratories for Mathematical Statistics written by Jenny A. Baglivo and published by SIAM. This book was released on 2005-01-01 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating computers into mathematical statistics courses allows students to simulate experiments and visualize their results, handle larger data sets, analyze data more quickly, and compare the results of classical methods of data analysis with those using alternative techniques. This text presents a concise introduction to the concepts of probability theory and mathematical statistics. The accompanying in-class and take-home computer laboratory activities reinforce the techniques introduced in the text and are accessible to students with little or no experience with Mathematica. These laboratory materials present applications in a variety of real-world settings, with data from epidemiology, environmental sciences, medicine, social sciences, physical sciences, manufacturing, engineering, marketing, and sports. Mathematica Laboratories for Mathematical Statistics: Emphasizing Simulation and Computer Intensive Methods includes parametric, nonparametric, permutation, bootstrap and diagnostic methods. Chapters on permutation and bootstrap techniques follow the formal inference chapters and precede the chapters on intermediate-level topics. Permutation and bootstrap methods are discussed side by side with classical methods in the later chapters.

Download or read book An Introduction to Statistics with the Wolfram Language written by Juan H. Klopper and published by Wolfram Media Incorporated. This book was released on 2020-05-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Download or read book Hands on Start to Wolfram Mathematica written by Cliff Hastings and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than 25 years, Mathematica has been the principal computation environment for millions of innovators, educators, students, and others around the world. This book is an introduction to Mathematica. The goal is to provide a hands-on experience introducing the breadth of Mathematica with a focus on ease of use. Readers get detailed instruction with examples for interactive learning and end-of-chapter exercises. Each chapter also contains authors' tips from their combined 50+ years of Mathematica use.

Download or read book Introduction to Probability with Mathematica written by Kevin J. Hastings and published by CRC Press. This book was released on 2009-09-21 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated to conform to Mathematica® 7.0, Introduction to Probability with Mathematica®, Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanying CD-ROM offers instructors the option of creating class notes, demonstrations, and projects. New to the Second Edition Expanded section on Markov chains that includes a study of absorbing chains New sections on order statistics, transformations of multivariate normal random variables, and Brownian motion More example data of the normal distribution More attention on conditional expectation, which has become significant in financial mathematics Additional problems from Actuarial Exam P New appendix that gives a basic introduction to Mathematica New examples, exercises, and data sets, particularly on the bivariate normal distribution New visualization and animation features from Mathematica 7.0 Updated Mathematica notebooks on the CD-ROM (Go to Downloads/Updates tab for link to CD files.) After covering topics in discrete probability, the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions, including normal, bivariate normal, gamma, and chi-square distributions. The author goes on to examine the history of probability, the laws of large numbers, and the central limit theorem. The final chapter explores stochastic processes and applications, ideal for students in operations research and finance.

Download or read book Statistical Inference written by Helio S. Migon and published by CRC Press. This book was released on 2014-09-03 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Balanced Treatment of Bayesian and Frequentist Inference Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition. New to the Second Edition New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and exercises More comparison between the approaches, including their similarities and differences Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.

Download or read book Mathematica Data Analysis written by Sergiy Suchok and published by Packt Publishing Ltd. This book was released on 2015-12-24 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn and explore the fundamentals of data analysis with power of Mathematica About This Book Use the power of Mathematica to analyze data in your applications Discover the capabilities of data classification and pattern recognition offered by Mathematica Use hundreds of algorithms for time series analysis to predict the future Who This Book Is For The book is for those who want to learn to use the power of Mathematica to analyze and process data. Perhaps you are already familiar with data analysis but have never used Mathematica, or you know Mathematica but you are new to data analysis. With the help of this book, you will be able to quickly catch up on the key points for a successful start. What You Will Learn Import data from different sources to Mathematica Link external libraries with programs written in Mathematica Classify data and partition them into clusters Recognize faces, objects, text, and barcodes Use Mathematica functions for time series analysis Use algorithms for statistical data processing Predict the result based on the observations In Detail There are many algorithms for data analysis and it's not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel. Style and approach This book takes a step-by-step approach, accompanied by examples, so you get a better understanding of the logic of writing algorithms for data analysis in Mathematica. We provide a detailed explanation of all the nuances of the Mathematica language, no matter what your level of experience is.

Download or read book The Student s Introduction to MATHEMATICA written by Bruce F. Torrence and published by Cambridge University Press. This book was released on 1999-05-13 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a brief introduction to those aspects of Mathematica most needed by students.

Download or read book Bayesian Logical Data Analysis for the Physical Sciences written by Phil Gregory and published by Cambridge University Press. This book was released on 2005-04-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Download or read book Mathematica Data Visualization written by Nazmus Saquib and published by Packt Publishing Ltd. This book was released on 2014-09-25 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are planning to create data analysis and visualization tools in the context of science, engineering, economics, or social science, then this book is for you. With this book, you will become a visualization expert, in a short time, using Mathematica.

Download or read book Modern Multivariate Statistical Techniques written by Alan J. Izenman and published by Springer Science & Business Media. This book was released on 2009-03-02 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

Download or read book Mathematica by Example written by Martha L Abell and published by Academic Press. This book was released on 2014-05-09 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematica by Example presents the commands and applications of Mathematica, a system for doing mathematics on a computer. This text serves as a guide to beginning users of Mathematica and users who do not intend to take advantage of the more specialized applications of Mathematica. The book combines symbolic manipulation, numerical mathematics, outstanding graphics, and a sophisticated programming language. It is comprised of 10 chapters. Chapter 1 gives a brief background of the software and how to install it in the computer. Chapter 2 introduces the essential commands of Mathematica. Basic operations on numbers, expressions, and functions are introduced and discussed. Chapter 3 provides Mathematica's built-in calculus commands. The fourth chapter presents elementary operations on lists and tables. This chapter is a prerequisite for Chapter 5 which discusses nested lists and tables in detail. The purpose of Chapter 6 is to illustrate various computations Mathematica can perform when solving differential equations. Chapters 7, 8, and 9 introduce Mathematica Packages that are not found in most Mathematica reference book. The final chapter covers the Mathematica Help feature. Engineers, computer scientists, physical scientists, mathematicians, business professionals, and students will find the book useful.

Download or read book Theory of Spatial Statistics written by M.N.M. van Lieshout and published by CRC Press. This book was released on 2019-03-19 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix. Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers. Features * Presents the mathematical foundations of spatial statistics. * Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology. * Gives pointers to the literature to facilitate further study. * Provides example code in R to encourage the student to experiment. * Offers exercises and their solutions to test and deepen understanding. The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.