Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Introduction To Statistics With The Wolfram Language PDF full book. Access full book title Introduction To Statistics With The Wolfram Language.
| Author | : Juan H. Klopper |
| Publisher | : Wolfram Media Incorporated |
| Total Pages | : |
| Release | : 2020-05-06 |
| Genre | : |
| ISBN | : 9781579550332 |
| Author | : Juan H. Klopper |
| Publisher | : Wolfram Media Incorporated |
| Total Pages | : |
| Release | : 2021 |
| Genre | : Computers |
| ISBN | : 9781579550349 |
In Introduction to Statistics with the Wolfram Language, Juan Klopper has applied the extensive computational power of the Wolfram Language to the field of statistics. Starting from the basics and working through a range of foundational concepts, this book shows just how accessible computational statistics can be given the appropriate tool set. Follow along as Klopper offers step-by-step instructions and relevant exercises from the field of medical statistics designed to teach readers how to understand and present data in today's increasingly data-driven world.The Wolfram Language represents a major advance in programming languages that makes leading-edge computation accessible to everyone. Unique in its approach of building in vast knowledge and automation, the Wolfram Language scales from a single line of easy-to-understand interactive code to million-line production systems. wolfram.com/language
| Author | : Stephen Wolfram |
| Publisher | : Wolfram Research, Incorporated |
| Total Pages | : 0 |
| Release | : 2017 |
| Genre | : Mathematica (Computer file). |
| ISBN | : 9781944183059 |
The Wolfram Language represents a major advance in programming languages that makes leading-edge computation accessible to everyone. Unique in its approach of building in vast knowledge and automation, the Wolfram Language scales from a single line of easy-to-understand interactive code to million-line production systems. This book provides an elementary introduction to the Wolfram Language and modern computational thinking. It assumes no prior knowledge of programming, and is suitable for both technical and non-technical college and high-school students, as well as anyone with an interest in the latest technology and its practical application.
| Author | : Martha L. Abell |
| Publisher | : Academic Press |
| Total Pages | : 654 |
| Release | : 1999 |
| Genre | : Computers |
| ISBN | : 9780120415540 |
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
| Author | : Cliff Hastings |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2016 |
| Genre | : Computers |
| ISBN | : 9781579550127 |
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.
| Author | : Shan-e-Fatima |
| Publisher | : Blue Rose Publishers |
| Total Pages | : 189 |
| Release | : 2023-09-25 |
| Genre | : Education |
| ISBN | : |
With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programmers may predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input. Machine learning is frequently used in recommendation engines. Business process automation (BPA), predictive maintenance, spam filtering, malware threat detection, and fraud detection are a few additional common uses. Machine learning is significant because it aids in the development of new goods and provides businesses with a picture of trends in consumer behavior and operational business patterns. For many businesses, machine learning has emerged as a key competitive differentiation. The fundamental methods of machine learning are covered in the current book.
| Author | : Colin Rose |
| Publisher | : Springer |
| Total Pages | : 508 |
| Release | : 2002 |
| Genre | : Business & Economics |
| ISBN | : 9780387952345 |
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.
| Author | : Bruce F. Torrence |
| Publisher | : Cambridge University Press |
| Total Pages | : 550 |
| Release | : 2019-05-16 |
| Genre | : Computers |
| ISBN | : 110831404X |
The unique feature of this compact student's introduction to Mathematica® and the Wolfram LanguageTM is that the order of the material closely follows a standard mathematics curriculum. As a result, it provides a brief introduction to those aspects of the Mathematica® software program most useful to students. Used as a supplementary text, it will help bridge the gap between Mathematica® and the mathematics in the course, and will serve as an excellent tutorial for former students. There have been significant changes to Mathematica® since the second edition, and all chapters have now been updated to account for new features in the software, including natural language queries and the vast stores of real-world data that are now integrated through the cloud. This third edition also includes many new exercises and a chapter on 3D printing that showcases the new computational geometry capabilities that will equip readers to print in 3D.
| Author | : Bruce F. Torrence |
| Publisher | : Cambridge University Press |
| Total Pages | : 484 |
| Release | : 2009-01-29 |
| Genre | : Computers |
| ISBN | : 1139473735 |
The unique feature of this compact student's introduction is that it presents concepts in an order that closely follows a standard mathematics curriculum, rather than structure the book along features of the software. As a result, the book provides a brief introduction to those aspects of the Mathematica software program most useful to students. The second edition of this well loved book is completely rewritten for Mathematica 6 including coverage of the new dynamic interface elements, several hundred exercises and a new chapter on programming. This book can be used in a variety of courses, from precalculus to linear algebra. Used as a supplementary text it will aid in bridging the gap between the mathematics in the course and Mathematica. In addition to its course use, this book will serve as an excellent tutorial for those wishing to learn Mathematica and brush up on their mathematics at the same time.
| Author | : Jalil Villalobos Alva |
| Publisher | : Apress |
| Total Pages | : |
| Release | : 2021-03-28 |
| Genre | : Computers |
| ISBN | : 9781484265932 |
Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book introduces you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering The fundamentals of the Wolfram Neural Network Framework and how to build your neural network for different tasks How to use pre-trained models from the Wolfram Neural Net Repository Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.
