Dive into the realm of predictive analytics with this R-focused guide. Whether you're building your first model or refining complex analytics strategies, this book equips you with fundamental techniques and in-depth understanding of predictive modeling using R. What this Book will help me do Master the end-to-end predictive modeling process. Classify and select suitable predictive models for specific use cases. Understand the mechanics and assumptions of various predictive models. Evaluate predictive model performance with appropriate metrics. Enhance your R programming skills for analytical tasks. Author(s) The authors of this book combine strong technical expertise in data science and predictive analytics with extensive hands-on experience in applying them to real-world challenges. They excel at distilling complex topics into approachable, actionable steps for readers at varying levels of familiarity with R and data analysis. Their commitment to empowering learners defines their work. Who is it for? This book is perfect for budding data scientists and quantitative analysts with basic R knowledge who aspire to master predictive analytics. Even experienced professionals will find valuable model-specific insights. If you're familiar with basic statistics and eager to bridge the gap to robust machine learning applications, this book is for you.

This is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. The book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the tools required to exploit recent advances in methodology when assessing the health risks associated with environmental hazards. The text includes practical examples together with embedded R code, details of specific R packages, and other software, including WinBUGS/OpenBUGS and INLA.

Here's the net takeaway: Businesses want insights from data they can translate into meaningful actions and real results. Software vendors are beginning to deliver a new generation of advanced analytics packages that address business issues directly. In this O'Reilly report, Mike Barlow reveals how this new user-friendly software is helping businesses go beyond data analysis and straight to decision-making—without requiring data science expertise or truckloads of cash. How has advanced analytics progressed from lab project to commercial product so quickly? Through interviews with data analysts, you'll understand the role that machine learning plays in specialized analytics packages, and how this software alone can make decisions based on what's likely to happen next. When you have these capabilities, you’ve reached "the last mile of analytics."

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.

"Mastering Pandas for Finance" takes a deep dive into applying Python and the pandas library to solve real-world financial data analysis problems. With a focus on financial modeling, backtesting trading strategies, and analyzing large datasets, this book equips you with the skills to leverage pandas effectively. What this Book will help me do Utilize pandas DataFrame for efficient financial data handling and manipulation. Develop robust time-series models and perform statistical analysis on financial data. Backtest algorithmic trading strategies including momentum and mean reversion. Price complex financial options and calculate Value at Risk for portfolio management. Optimize portfolio allocation and model financial performance using industry techniques. Author(s) Michael Heydt is an experienced software engineer and data scientist with a strong background in quantitative finance. He specializes in using Python for data analysis and has spent years teaching and writing about technical subjects. His detailed yet approachable writing style makes complex topics accessible to all. Who is it for? "Mastering Pandas for Finance" is perfect for finance professionals seeking to integrate Python into their workflows, data analysts exploring quantitative finance applications, and programmers aiming to specialize in financial analytics. Some baseline Python and pandas knowledge is recommended, but the book is structured to guide you effectively through advanced concepts too.

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.

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. About the Technology Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide. About the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing. What's Inside Complete R language tutorial Using R to manage, analyze, and visualize data Techniques for debugging programs and creating packages OOP in R Over 160 graphs About the Reader This book is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required. About the Author Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net. Quotes Essential to anyone doing data analysis with R, whether in industry or academia. - Cristofer Weber, NeoGrid A go-to reference for general R and many statistics questions. - George Gaines, KYOS Systems Inc. Accessible language, realistic examples, and clear code. - Samuel D. McQuillin, University of Houston Offers a gentle learning curve to those starting out with R for the first time. - Indrajit Sen Gupta, Mu Sigma Business Solutions

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.

Data-driven technologies are now being adopted, developed, funded, and deployed throughout the health care market at an unprecedented scale. But, as this O'Reilly report reveals, health care innovation contains more hurdles and requires more finesse than many tech startups expect. By paying attention to the lessons from the report's findings, innovation teams can better anticipate what they'll face, and plan accordingly. Simply put, teams looking to apply collective intelligence and "big data" platforms to health and health care problems often don't appreciate the messy details of using and making sense of data in the heavily regulated hospital IT environment. Download this report today and learn how it helps prepare startups in six areas: Complexity: An enormous domain with noisy data not designed for machine consumption Computing: Lack of standard, interoperable schema for documenting human health in a digital format Context: Lack of critical contextual metadata for interpreting health data Culture: Startup difficulties in hospital ecosystems: why innovation can be a two-edged sword Contracts: Navigating the IRB, HIPAA, and EULA frameworks Commerce: The problem of how digital health startups get paid This report represents the initial findings of a study funded by a grant from the Robert Wood Johnson Foundation. Subsequent reports will explore the results of three deep-dive projects the team pursued during the study.

An abundance of real-world examples highlights Lauren Haworth Lake’s and Julie McKnight's PROC TABULATE by Example, Second Edition. Beginning and intermediate SAS® users will find this step-by-step guide to producing tables and reports using the TABULATE procedure both convenient and inviting. Topics are presented in order of increasing complexity, making this an excellent training manual or self-tutorial. The concise format also makes this a quick reference guide for specific applications for more advanced users. A very handy section on common problems and their solutions is also included. With this book, you will quickly learn how to generate tables using macros, handle percentages and missing data, modify row and column headings, and produce one-, two-, and three-dimensional tables using PROC TABULATE. Also provided are more advanced tips on complex formatting with the Output Delivery System (ODS) and exporting PROC TABULATE output to other applications.

Now a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. , Building on Miller's pioneering program, thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Marketing Data Science Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance. Marketing Data Science

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.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self-contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self-adjoint and non self-adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.

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.

Get a complete view of your customers and make your marketing analysis more meaningful How well do you really know your customers? Find out with the help of expert author Daniel Waisberg and Google Analytics Integrations. This unique guide takes you well beyond the basics of using Google Analytics to track metrics, showing you how to transform this simple data collection tool into a powerful, central marketing analysis platform for your organization. You'll learn how Google AdWords, AdSense, CRMs, and other data sources can be used together to deliver actionable insights about your customers and their behavior. Explains proven techniques and best practices for collecting clean and accurate information from the start Shows you how to import your organization's marketing and customer data into Google Analytics Illustrates the importance of taking a holistic view of your customers and how this knowledge can transform your business Provides step-by-step guidance on using the latest analytical tools and services to gain a complete understanding of your customers, their needs, and what motivates them to take action Google Analytics Integration is your in-depth guide to improving your data integration, behavioral analysis, and ultimately, your bottom line.

Learning Tableau is your guide to mastering Tableau 9.0 for building impactful data visualizations and creating interactive, insightful dashboards. Whether you're beginning your data visualization journey or seek to refine your skills, this book provides a comprehensive approach to unlocking the potential of your data. What this Book will help me do Understand how to create basic and advanced visualizations for effective data representation. Learn techniques to enhance data analysis through custom calculations and interactive features. Gain skills to integrate and analyze data from multiple sources using Tableau's blending and joining features. Master the art of designing and formatting visually appealing dashboards to tell compelling data stories. Explore advanced Tableau functionalities like LOD calculations and sheet swapping to improve analytical insights. Author(s) Joshua N. Milligan is a seasoned data professional with a wealth of experience in Tableau. As a Tableau Zen Master, he combines his practical expertise and in-depth knowledge to craft a beginner-friendly guide for professionals looking to grasp Tableau's features effectively. His approachable and engaging writing style makes complex concepts accessible. Who is it for? This book is ideal for professionals and enthusiasts aiming to develop their skills in data visualization and Tableau. It's suitable for individuals with basic knowledge of data or databases, but no prior Tableau experience is necessary. Whether you're a data analyst, business professional, or IT specialist, this book will help you communicate data insights clearly. Readers will achieve a comprehensive understanding of Tableau 9.0's capabilities to create valuable outcomes.

This book explains the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, exercises, and sample solutions encompass practical topics pertaining to data processing and analysis. The book can be used for self-study or as supplementary reading in a statistical computing course, allowing students to gain valuable data science skills.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Topics addressed include: Appropriate methods for binary, ordinal, and continuous measures Computations using PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros Comparing the ROC curves of several markers and adjusting them for covariates ROC curves with censored data Using the ROC curve for evaluating multivariable prediction models via bootstrap and cross-validation ROC curves in SAS Enterprise Miner And more! Written for any statistician interested in learning more about ROC curve methodology, the book assumes readers have a basic understanding of regression procedures and moderate familiarity with Base SAS and SAS/STAT. Some familiarity with SAS/GRAPH is helpful but not essential. This book is part of the SAS Press program.

Thoroughly updated for SAS 9, Cody's Data Cleaning Techniques Using SAS, Second Edition, addresses tasks that nearly every SAS programmer needs to do - that is, make sure that data errors are located and corrected. Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify for your own special data cleaning needs. Each topic is developed through specific examples, and every program and macro is explained in detail.

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.

"Learning Pandas" is your comprehensive guide to mastering pandas, the powerful Python library for data manipulation and analysis. In this book, you'll explore pandas' capabilities and learn to apply them to real-world data challenges. With clear explanations and hands-on examples, you'll enhance your ability to analyze, clean, and visualize data effectively. What this Book will help me do Understand the core concepts of pandas and how it integrates with Python. Learn to efficiently manipulate and transform datasets using pandas. Gain skills in analyzing and cleaning data to prepare for insights. Explore techniques for working with time-series data and financial datasets. Discover how to create compelling visualizations with pandas to communicate findings. Author(s) Michael Heydt is an experienced Python developer and data scientist with expertise in teaching technical concepts to others. With a deep understanding of the pandas library, Michael has authored several guides on data analysis and is passionate about making complex information accessible. His practical approach ensures readers can directly apply lessons to their own projects. Who is it for? This book is ideal for Python programmers who want to harness the power of pandas for data analysis. Whether you're a beginner in data science or looking to refine your skills, you'll find clear, actionable guidance here. Basic programming knowledge is assumed, but no prior pandas experience is necessary. If you're eager to turn data into impactful insights, this book is for you.