Partial Least Squares (PLS) is a flexible statistical modeling technique that applies to data of any shape. It models relationships between inputs and outputs even when there are more predictors than observations. Using JMP statistical discovery software from SAS, Discovering Partial Least Squares with JMP explores PLS and positions it within the more general context of multivariate analysis. Ian Cox and Marie Gaudard use a "learning through doing" style. This approach, coupled with the interactivity that JMP itself provides, allows you to actively engage with the content. Four complete case studies are presented, accompanied by data tables that are available for download. The detailed "how to" steps, together with the interpretation of the results, help to make this book unique. Discovering Partial Least Squares with JMP is of interest to professionals engaged in continuing development, as well as to students and instructors in a formal academic setting. The content aligns well with topics covered in introductory courses on: psychometrics, customer relationship management, market research, consumer research, environmental studies, and chemometrics. The book can also function as a supplement to courses in multivariate statistics, and to courses on statistical methods in biology, ecology, chemistry, and genomics. While the book is helpful and instructive to those who are using JMP, a knowledge of JMP is not required, and little or no prior statistical knowledge is necessary. By working through the introductory chapters and the case studies, you gain a deeper understanding of PLS and learn how to use JMP to perform PLS analyses in real-world situations. This book motivates current and potential users of JMP to extend their analytical repertoire by embracing PLS. Dynamically interacting with JMP, you will develop confidence as you explore underlying concepts and work through the examples. The authors provide background and guidance to support and empower you on this journey.

Implement machine learning, time-series analysis, algorithmic trading and more About This Book * Understand the basics of R and how they can be applied in various Quantitative Finance scenarios * Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. * Contain different methods to manage risk and explore trading using Machine Learning. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn * Get to know the basics of R and how to use it in the field of Quantitative Finance * Understand data processing and model building using R * Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis * Build and analyze quantitative finance models using real-world examples * How real-life examples should be used to develop strategies * Performance metrics to look into before deciding upon any model * Deep dive into the vast world of machine-learning based trading * Get to grips with algorithmic trading and different ways of optimizing it * Learn about controlling risk parameters of financial instruments In Detail The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book * Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data * Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images * A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn * Acquire, format, and visualize your data * Build an image-similarity search engine * Generate meaningful visualizations anyone can understand * Get started with analyzing social network graphs * Find out how to implement sentiment text analysis * Install data analysis tools such as Pandas, MongoDB, and Apache Spark * Get to grips with Apache Spark * Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

With a growing number of scientists and engineers using JMP software for design of experiments, there is a need for an example-driven book that supports the most widely used textbook on the subject, Design and Analysis of Experiments by Douglas C. Montgomery. Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP meets this need and demonstrates all of the examples from the Montgomery text using JMP. In addition to scientists and engineers, undergraduate and graduate students will benefit greatly from this book. While users need to learn the theory, they also need to learn how to implement this theory efficiently on their academic projects and industry problems. In this first book of its kind using JMP software, Rushing, Karl and Wisnowski demonstrate how to design and analyze experiments for improving the quality, efficiency, and performance of working systems using JMP. Topics include JMP software, two-sample t-test, ANOVA, regression, design of experiments, blocking, factorial designs, fractional-factorial designs, central composite designs, Box-Behnken designs, split-plot designs, optimal designs, mixture designs, and 2 k factorial designs. JMP platforms used include Custom Design, Screening Design, Response Surface Design, Mixture Design, Distribution, Fit Y by X, Matched Pairs, Fit Model, and Profiler. With JMP software, Montgomery's textbook, and Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP, users will be able to fit the design to the problem, instead of fitting the problem to the design.

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book * Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages * Understand how to apply useful data analysis techniques in R for real-world applications * An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn * Get to know the functional characteristics of R language * Extract, transform, and load data from heterogeneous sources * Understand how easily R can confront probability and statistics problems * Get simple R instructions to quickly organize and manipulate large datasets * Create professional data visualizations and interactive reports * Predict user purchase behavior by adopting a classification approach * Implement data mining techniques to discover items that are frequently purchased together * Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the "dplyr" and "data.table" packages to efficiently process larger data structures. We also focus on "ggplot2" and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the "ggvis" package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

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. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.

For courses in Political and Social Statistics Using the popular SPSS software to de-mystify statistics Using SPSS for Windows and Macintosh guides students through basic SPSS techniques, using step-by-step descriptions and explaining in detail how to avoid common pitfalls in the study of statistics. Authors Samuel Green and Neil Salkind provide extensive substantive information about each statistical technique, including a brief discussion of the technique, examples of how the statistic is applied, a sample data set that can be analyzed with the statistic, a discussion of the analysis results, practice exercises, and more. The Eighth Edition has been updated for SPSS version 23 (Windows/Mac), and now offers added accessibility and increased problem solving. NOTE: This ISBN is for a Pearson Books a la Carte edition: a convenient, three-hole-punched, loose-leaf text. In addition to the flexibility offered by this format, Books a la Carte editions offer students great value, as they cost significantly less than a bound textbook.

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.

Load, wrangle, and analyze your data using the world's most powerful statistical programming language About This Book * Load, manipulate and analyze data from different sources * Gain a deeper understanding of fundamentals of applied statistics * A practical guide to performing data analysis in practice Who This Book Is For Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn * Navigate the R environment * Describe and visualize the behavior of data and relationships between data * Gain a thorough understanding of statistical reasoning and sampling * Employ hypothesis tests to draw inferences from your data * Learn Bayesian methods for estimating parameters * Perform regression to predict continuous variables * Apply powerful classification methods to predict categorical data * Handle missing data gracefully using multiple imputation * Identify and manage problematic data points * Employ parallelization and Rcpp to scale your analyses to larger data * Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with "messy data", large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach.

Harness the power of RStudio to create web applications, R packages, markdown reports and pretty data visualizations About This Book * Discover the multi-functional use of RStudio to support your daily work with R code * Learn to create stunning, meaningful, and interactive graphs and learn to embed them into easy communicable reports using multiple R packages * Develop your own R packages and Shiny web apps to share your knowledge and collaborate with others Who This Book Is For This book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio's functionality to ease their development efforts. R programming experience is assumed as well as being comfortable with R's basic structures and a number of functions. What You Will Learn * Discover the RStudio IDE and details about the user interface * Communicate your insights with R Markdown in static and interactive ways * Learn how to use different graphic systems to visualize your data * Build interactive web applications with the Shiny framework to present and share your results * Understand the process of package development and assemble your own R packages * Easily collaborate with other people on your projects by using Git and GitHub * Manage the R environment for your organization with RStudio and Shiny server * Apply your obtained knowledge about RStudio and R development to create a real-world dashboard solution In Detail RStudio helps you to manage small to large projects by giving you a multi-functional integrated development environment, combined with the power and flexibility of the R programming language, which is becoming the bridge language of data science for developers and analyst worldwide. Mastering the use of RStudio will help you to solve real-world data problems. This book begins by guiding you through the installation of RStudio and explaining the user interface step by step. From there, the next logical step is to use this knowledge to improve your data analysis workflow. We will do this by building up our toolbox to create interactive reports and graphs or even web applications with Shiny. To collaborate with others, we will explore how to use Git and GitHub with RStudio and how to build your own packages to ensure top quality results. Finally, we put it all together in an interactive dashboard written with R. Style and approach An easy-to-follow guide full of hands-on examples to master RStudio. Beginning from explaining the basics, each topic is explained with a lot of details for every feature.

DESCRIPTION The TI-84 Plus series graphing calculators is are the de facto standard for graphing calculators used by students in grades 6 through college. With so many features and functions, the TI-84 Plus graphing calculator can be a little intimidating. Using the TI-84 Plus is an easy-to-follow guide to using these calculators for class and for the SAT and ACT. It starts with a hands-on orientation to the calculator so readers will be comfortable with its menus, buttons, and the special vocabulary it uses. Then, it explores key features while tackling problems just like the ones seen in math and sciences classes. TI-84 Plus calculators are permitted on most standardized tests, so the book provides specific guidance for SAT and ACT math. Along the way, easy-to-find reference sidebars offer skills in a nutshell for those times when just a quick reminder is needed. KEY SELLING POINTS Includes coverage of the brand-new TI-84 Plus CE For TI-83 Plus and TI-84 Plus series of graphing calculators The "missing manual" for the TI-84 Plus calculators Gets readers up and running on calculators fast Fun, engaging, and approachable examples Easy hands-on learn by doing approach AUDIENCE This book is written for students, teachers-anyone who wants to use the TI-84 Plus or TI-83 Plus of graphing calculators. No prior experience is needed and it assumes no advanced knowledge of math and science. ABOUT THE TECHNOLOGY The TI-84 Plus series is the de facto standard for graphing calculators used by students in grades 6 through college and for standardized tests. These calculators can do everything from basic arithmetic through graphing, pre-calculus, calculus, statistics, and probability, and are even great tools for learning programming.

Explore the big data field and learn how to perform data analytics and predictive modelling in STATA About This Book * Visualize and analyse data in STATA to devise a business strategy * Learn STATA programming and predictive modeling * Discover how you can become a data scientist with the power of STATA Who This Book Is For This book is for all the professionals and students who want to learn STATA programming and apply predictive modelling concepts. This book is also very helpful for experienced STATA programmers as it provides advanced statistical modelling concepts and their application. What You Will Learn * Perform important statistical tests to become a STATA data scientist * Be guided through how to program in STATA * Implement logistic and linear regression models * Visualize and program the data in STATA * Analyse survey data, time series data, and survival data * Perform database management in STATA In Detail STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA. Style and approach This book is a hands-onguide to STATA programming and statistical modelling providing many STATA code examples and taking. You through the working of the code in detail.

Gain sharp insights into your data and solve real-world data science problems with R-from data munging to modeling and visualization About This Book * Handle your data with precision and care for optimal business intelligence * Restructure and transform your data to inform decision-making * Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn * Connect to and load data from R's range of powerful databases * Successfully fetch and parse structured and unstructured data * Transform and restructure your data with efficient R packages * Define and build complex statistical models with glm * Develop and train machine learning algorithms * Visualize social networks and graph data * Deploy supervised and unsupervised classification algorithms * Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.