The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.

Bent Functions: Results and Applications to Cryptography offers a unique survey of the objects of discrete mathematics known as Boolean bent functions. As these maximal, nonlinear Boolean functions and their generalizations have many theoretical and practical applications in combinatorics, coding theory, and cryptography, the text provides a detailed survey of their main results, presenting a systematic overview of their generalizations and applications, and considering open problems in classification and systematization of bent functions. The text is appropriate for novices and advanced researchers, discussing proofs of several results, including the automorphism group of bent functions, the lower bound for the number of bent functions, and more. Provides a detailed survey of bent functions and their main results, presenting a systematic overview of their generalizations and applications Presents a systematic and detailed survey of hundreds of results in the area of highly nonlinear Boolean functions in cryptography Appropriate coverage for students from advanced specialists in cryptography, mathematics, and creators of ciphers

This book explains how to analyze statistical models with hidden (latent) variables. It takes a systematic, geometric approach to studying the semialgebraic structure of latent tree models. The first part of the book introduces key concepts in algebraic statistics, focusing on methods that are helpful in the study of models with hidden variables. The second part illustrates important examples of tree models with hidden variables. The author develops the important concepts of L-cumulants and links latent tree models and various tree spaces.

Make studying statistics simple with this easy-to-read resource Wouldn't it be wonderful if studying statistics were easier? With U Can: Statistics I For Dummies, it is! This one-stop resource combines lessons, practical examples, study questions, and online practice problems to provide you with the ultimate guide to help you score higher in your statistics course. Foundational statistics skills are a must for students of many disciplines, and leveraging study materials such as this one to supplement your statistics course can be a life-saver. Because U Can: Statistics I For Dummies contains both the lessons you need to learn and the practice problems you need to put the concepts into action, you'll breeze through your scheduled study time. Statistics is all about collecting and interpreting data, and is applicable in a wide range of subject areas—which translates into its popularity among students studying in diverse programs. So, if you feel a bit unsure in class, rest assured that there is an easy way to help you grasp the nuances of statistics! Understand statistical ideas, techniques, formulas, and calculations Interpret and critique graphs and charts, determine probability, and work with confidence intervals Critique and analyze data from polls and experiments Combine learning and applying your new knowledge with practical examples, practice problems, and expanded online resources U Can: Statistics I For Dummies contains everything you need to score higher in your fundamental statistics course!

This book presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data. Many real examples from both mental and physical health disorders illustrate the use of the methods.

This practical and thorough book explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.

This unique and insightful book examines leadership within the roles of statistician and data scientist from international and diverse perspectives. Featuring contributions from leadership experts and statisticians at various stages on the career jungle gym, the text supplies a greater understanding of leadership within teams, research consulting, and project management. It encourages reflection on leadership behaviors, promoting natural and organizational leadership, identifying existing opportunities to foster creative outputs and develop strong leadership voices, and explaining how to convert a passion for statistical science into visionary, ethical, and transformational leadership.

" If you have trouble estimating cost or schedule for your projects, you are not alone. The question is this: who wants the estimate and why? The definition of estimate is to guess. But too often, the people who want estimates want commitments. Instead of a commitment, you can apply practical and pragmatic approaches to developing estimates and then meet your commitments. You can provide your managers with the information they want and that you can live with. Learn how to use different words for your estimates and how to report an estimate that includes uncertainty. Learn who should and should not estimate. Learn how to update your estimate when you know more about your project. Regain estimation sanity. Learn practical and pragmatic ways to estimate schedule or cost for your projects."

This book provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It attempts to establish a gold standard for how a statistical analysis should proceed. The book illustrates relative belief theory using many examples and describes the strengths and weaknesses of the theory.

A unique, in-depth guide to options pricing and valuing their greeks, along with a four dimensional approach towards the impact of changing market circumstances on options How to Calculate Options Prices and Their Greeks is the only book of its kind, showing you how to value options and the greeks according to the Black Scholes model but also how to do this without consulting a model. You'll build a solid understanding of options and hedging strategies as you explore the concepts of probability, volatility, and put call parity, then move into more advanced topics in combination with a four-dimensional approach of the change of the P&L of an option portfolio in relation to strike, underlying, volatility, and time to maturity. This informative guide fully explains the distribution of first and second order Greeks along the whole range wherein an option has optionality, and delves into trading strategies, including spreads, straddles, strangles, butterflies, kurtosis, vega-convexity, and more. Charts and tables illustrate how specific positions in a Greek evolve in relation to its parameters, and digital ancillaries allow you to see 3D representations using your own parameters and volumes. The Black and Scholes model is the most widely used option model, appreciated for its simplicity and ability to generate a fair value for options pricing in all kinds of markets. This book shows you the ins and outs of the model, giving you the practical understanding you need for setting up and managing an option strategy. Understand the Greeks, and how they make or break a strategy See how the Greeks change with time, volatility, and underlying Explore various trading strategies Implement options positions, and more Representations of option payoffs are too often based on a simple two-dimensional approach consisting of P&L versus underlying at expiry. This is misleading, as the Greeks can make a world of difference over the lifetime of a strategy. How to Calculate Options Prices and Their Greeks is a comprehensive, in-depth guide to a thorough and more effective understanding of options, their Greeks, and (hedging) option strategies.

The ultimate beginner's guide to SPSS and statistical analysis SPSS Statistics For Dummies is the fun and friendly guide to mastering SPSS. This book contains everything you need to know to get up and running quickly with this industry-leading software, with clear, helpful guidance on working with both the software and your data. Every chapter of this new edition has been updated with screenshots and steps that align with SPSS 23.0. You'll learn how to set up the software and organize your workflow, then delve deep into analysis to discover the power of SPSS capabilities. You'll discover the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and maximize your data, even if it's been awhile since your last statistics class. SPSS is the leading statistical software for social sciences, marketing, health care, demography, government, education, data mining, and more. This powerful package gives you the tools you need to get more out of your data, and this book is your beginner-friendly guide to getting the most out of the software. Install and configure SPSS and learn the basics of how it works Master the process of getting data into SPSS and manipulating it to produce results See how to display data in dozens of different graphic formats to fit specific needs Make SPSS manufacture the numbers you want and take advantage of the many analysis options Discover ways to customize the SPSS interface and the look of your results, edit graphics and pivot tables, and program SPSS with Command Syntax Statistical analysis is crucial to so many industries, and accuracy and efficiency are crucial. SPSS offers you the capability to deliver, but you still must know how to take utmost advantage of the tools at your fingertips. SPSS Statistics For Dummies shows you how to handle data like a pro, with step-by-step instruction and expert advice.

Collecting Bayesian material scattered throughout the literature, this volume examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained, gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples.

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. This book fills that void. Instead of delving into overly complex statistics, it focuses on simpler solutions that are just as effective. The authors not only explain the required statistical methods, but also supply detailed examples, stories, and case studies that facilitate the understanding required to apply those methods in real-world software engineering applications.

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

Bridging the gap between statistics texts and SAS documentation, Elementary Statistics Using SAS is written for those who want to perform analyses to solve problems. The first section of the book explains the basics of SAS data sets and shows how to use SAS for descriptive statistics and graphs. The second section discusses fundamental statistical concepts, including normality and hypothesis testing. The remaining sections of the book show analyses for comparing two groups, comparing multiple groups, fitting regression equations, and exploring contingency tables. For each analysis, author Sandra Schlotzhauer explains assumptions, statistical approach, and SAS methods and syntax, and makes conclusions from the results. Statistical methods covered include two-sample t-tests, paired-difference t-tests, analysis of variance, multiple comparison techniques, regression, regression diagnostics, and chi-square tests. Elementary Statistics Using SAS is a thoroughly revised and updated edition of Ramon Littell and Sandra Schlotzhauer's SAS System for Elementary Statistical Analysis. This book is part of the SAS Press program.

Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life's important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers' understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.

In Statistical Programming in SAS, author A. John Bailer integrates SAS tools with interesting statistical applications and uses SAS 9.2 as a platform to introduce programming ideas for statistical analysis, data management, and data display and simulation. Written using a reader-friendly and narrative style, the book includes extensive examples and case studies to present a well-structured introduction to programming issues.

This book is designed for engineering students studying for the core paper on probability and statistics. The topics have been dealt in a coherent manner, supported by illustrations for better compre¬hension. Each chapter is replete with examples and exercises. The book also has numerous Multiple Choice Questions at the end of each chapter, thus providing the student with an abundant repository of exam specific problems.

This book guides you through an enjoyable journey, step by step, into the future. A team of fictional characters is introduced to share their learning and working experiences with the readers. In the beginning of the book, you will take the first step by learning the most basic models for one-period forecasts based on past performance of a market. You will also learn how to evaluate your newly built models. Next, you will progress further into intermediate-level models, including multi-period forecasts based on past performance of a market or based on an external factor. It also introduces interval forecasting, which allows you to obtain a range of forecast values instead of a single value in the future. In the second half, you will familiarize yourself with advanced models that provide multi-period forecasts based on multiple internal or external factors. Toward the end, you will learn several applied models in business and economics that will facilitate you with practical applications related to real life situations. The  last chapter summarizes all models introduced in this book and provides a table of references for finding the most important concepts, tables, and figures in the book so that you can recall every step of your adventure.