Books: 56

Statistics

CoverTitleYear
Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary more » manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: * Provides a complete discussion of both the hardware and software used to organize big data * Describes a wide range of useful applications for managing big data and resultant data sets * Maintains a firm focus on massive data and large networks * Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology. « less
2016
A Guide to Solving Practical Problems
This is the first book to show the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn’t your strongest suit, more » you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Marketing Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned. « less
2016
An Object Oriented Approach Using C++
Use powerful C# algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Hedge fund managers cannot afford to ignore their risk/return profiles, and taking advantage of new technologies is an excellent way to minimize risk and capitalize on various investment styles. As Hedge more » Fund Analysis and Modeling Using C# demonstrates, the C# programming language is perfectly suited to hedge fund analysis. This book serves as a complete course in hedge fund modeling and provides a primer on C# and Object Oritented Programming (OOP) that will allow you to manage risk easily and make the most of key statistics. Covering both basic and risk-adjusted performance measures, Hedge Fund Analysis and Modeling Using C# moves from simple to sophisticated analysis techniques, using worked examples to show you exactly how to manage return in an era of volatility and financial risk. You'll have access to: * Complete guidance on using C# and Objected Oriented Programming (OOP) for analysis using non-normal returns data and other key statistics * Bonus content on a companion website containing C# programs, algorithms, and data available for download * Real world modeling exercises that demonstrate the identification of risk and return factors * Complete guidance for optimizing hedge fund decisions using quantitative strategies This is the only book on the market that guides you through using C# to model hedge fund risks and returns. Along with its companion titles on Excel/VBA analysis and MATLAB analysis, Hedge Fund Analysis and Modeling Using C# contributes important guidance for hedge fund managers who want to take advantage of technological platforms for optimal fund performance. « less
2016
This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided more » as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. « less
2016
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
ABOUT THIS BOOK * Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understanding * Leverage the flexibility and modularity of R to experiment with a range of different techniques and data types * Packed with practical advice and tips explaining more » important concepts and best practices to help you understand quickly and easily WHO THIS BOOK IS FOR This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level. WHAT YOU WILL LEARN * Master the steps involved in the predictive modeling process * Learn how to classify predictive models and distinguish which models are suitable for a particular problem * Understand how and why each predictive model works * Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem * Select appropriate metrics to assess the performance of different types of predictive model * Diagnose performance and accuracy problems when they arise and learn how to deal with them * Grow your expertise in using R and its diverse range of packages IN DETAIL R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics. « less
2015
A Hands-on Guide
Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big more » data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: * More than 200 examples and exercises, including code and datasets for practice. * Relevant examples for all industries. * Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before. WHAT YOU’LL LEARN * Which are the most important tools for performing analytics * How to program in SAS * How to explore, validate, and clean data * How to understand and use basic statistical methods and techniques * How to forecast future value using SAS * How to build predictive models * Fundamentals of big data WHO THIS BOOK IS FOR This book is for IT Professionals who want to become business or data analysts, predictive modelers, data scientists, social media analysts, big data analysts, or BI analysts. It's also for anyone who wants to break into data analytics or professionals who want to expand their skills. TABLE OF CONTENTS Part One: Basics of SAS Programming for Analytics Chapter 01 Introduction to Business Analytics and Data Analysis Tools Chapter 02: SAS Introduction Chapter 03: SAS Handling Using SAS Chapter 04 : Important SAS Functions and Procs Part Two: Using SAS for Business Analytics Chapter 05 Introduction to Statistical Analysis Chapter 06 Basic Descriptive Statistics Chapter 07 Data Exploration, Validation, and Data Sanitization Chapter 08 Testing of Hypothesis Chapter 09 Correlation and Linear Regression Chapter 10 Multiple Regression Analysis Chapter 11: Logistic Regression Chapter 12: Time Series Analysis and Forecasting Chapter 13: Introducing Big Data Analytics « less
2015
An Introduction Using R
A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R. This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to more » a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. « less
2014
Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each more » technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics. « less
2012
A Tutorial with R and BUGS
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students more » or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. -Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment « less
2010
Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience more » with its implementation, and practice with computational software. Focusing on the computational aspects of statistics rather than the theoretical, Computational Statistics Handbook with MATLAB uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors integrate the use of MATLAB throughout the book, allowing readers to see the actual implementation of algorithms, but also include step-by-step procedures to allow implementation with any suitable software. The book concentrates on the simulation/Monte Carlo point of view, and contains algorithms for exploratory data analysis, modeling, Monte Carlo simulation, pattern recognition, bootstrap, classification, cross-validation methods, probability density estimation, random number generation, and other computational statistics methods. Emphasis on the practical aspects of statistics, details of the latest techniques, and real implementation experience make the Computational Statistics Handbook with MATLAB more than just the first book to use MATLAB to solve computational problems in statistics. It also forms an outstanding, introduction to statistics for anyone in the many disciplines that involve data analysis. « less
2001