Probability, Statistics and Random Processes is designed to meet the requirements of students and is intended for beginners to help them understand the concepts from the first principles. Spread across 16 chapters, it discusses the theoretical aspects that have been refined and updated to reflect the current developments in the subjects. It expounds on theoretical concepts that have immense practical applications, giving adequate proofs to establish significant theorems.

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

Underwater Acoustic Modeling and Simulation, Fourth Edition continues to provide the most authoritative overview of currently available propagation, noise, reverberation, and sonar-performance models. This fourth edition of a bestseller discusses the fundamental processes involved in simulating the performance of underwater acoustic systems and emphasizes the importance of applying the proper modeling resources to simulate the behavior of sound in virtual ocean environments. New to the Fourth Edition Extensive new material that addresses recent advances in inverse techniques and marine-mammal protection Problem sets in each chapter Updated and expanded inventories of available models Designed for readers with an understanding of underwater acoustics but who are unfamiliar with the various aspects of modeling, the book includes sufficient mathematical derivations to demonstrate model formulations and provides guidelines for selecting and using the models. Examples of each type of model illustrate model formulations, model assumptions, and algorithm efficiency. Simulation case studies are also included to demonstrate practical applications. Providing a thorough source of information on modeling resources, this book examines the translation of our physical understanding of sound in the sea into mathematical models that simulate acoustic propagation, noise, and reverberation in the ocean. The text shows how these models are used to predict and diagnose the performance of complex sonar systems operating in the undersea environment.

Part of the Jones and Bartlett Learning International Series in Mathematics

Written for the one-term introductory probability and statistics course for mid- to upper-level math and science majors, Essentials of Mathematical Statistics combines the topics generally found in main-stream elementary statistics books with the essentials of the underlying theory. The book begins with an axiomatic treatment of probability followed by chapters on discrete and continuous random variables and their associated distributions. It then introduces basic statistical concepts including summarizing data and interval parameter estimation, stressing the connection between probability and statistics. Final chapters introduce hypothesis testing, regression, and non-parametric techniques. All chapters provide a balance between conceptual understanding and theoretical understanding of the topics at hand.

Key Features of Essentials of Mathematical Statistics:

• End-of-section exercises range from computational to conceptual to theoretical.
• Many sections include a sub-section titled “Software Calculations” which gives detailed descriptions of how to perform the calculations discussed in the section using the software Minitab, R, Excel, and the TI-83/84 calculators.
• Provides a clear balance between conceptual understanding and theoretical understanding
• Exercises throughout vary in level of difficulty and scope.

"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital One This book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques. You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales. How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt. In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. Why early retirement decreases life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death, including one health insurance company. How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance? Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.

is organized around applications, first introducing the actual behavior of specific signals and then using them to motivate the presentation of mathematical concepts. Tervo sequences the presentation of the major transforms by their complexity: first Fourier, then Laplace, and finally the z-transform. Practical Signals Theory with MATLAB Applications The goal is to help students who can't visualize phenomena from an equation to develop their intuition and learn to analyze signals by inspection. Finally, most examples and problems are designed to use MATLAB, making the presentation more in line with modern engineering practice.

Art Carpenter demystifies the powerful REPORT procedure and shows you how to incorporate this highly flexible and customizable procedure into your SAS reporting programs. Combining his years of SAS experience with a talent for instruction, Art offers clear and comprehensive coverage that demonstrates how valuable this procedure is for both summarizing and displaying data. Illustrated with more than two hundred examples and sample exercises to reinforce your learning, Carpenter's Complete Guide to the SAS REPORT Procedure provides you with information that you can put to immediate use. The text is divided into three distinct sections. Part 1 introduces you to PROC REPORT, showing you how it works and "thinks." This section is designed to be read linearly by users who are unfamiliar with the procedure. Part 2 is a collection of increasingly more complex examples that feature advanced options and capabilities. It also introduces the relationship between PROC REPORT and the Output Delivery System (ODS). Part 3 incorporates the options and statements described in Parts 1 and 2 into a series of examples that highlight many of the extended capabilities of PROC REPORT. Included in this section is a discussion of a few ODS statements and options that might be useful to a PROC REPORT programmer, plus an in-depth look at the PROC REPORT process itself, especially as it relates to the execution of compute blocks. Art's author page at support.sas.com/carpenter includes the following bonus material: example SAS data sets, example results, and a compilation of nearly 100 related conference papers. This book is part of the SAS Press program.

Chi-Squared Goodness of Fit Tests with Applications provides a thorough and complete context for the theoretical basis and implementation of Pearson’s monumental contribution and its wide applicability for chi-squared goodness of fit tests. The book is ideal for researchers and scientists conducting statistical analysis in processing of experimental data as well as to students and practitioners with a good mathematical background who use statistical methods. The historical context, especially Chapter 7, provides great insight into importance of this subject with an authoritative author team. This reference includes the most recent application developments in using these methods and models. Systematic presentation with interesting historical context and coverage of the fundamentals of the subject Presents modern model validity methods, graphical techniques, and computer-intensive methods Recent research and a variety of open problems Interesting real-life examples for practitioners

Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores notions and structures in probability, including combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as weak and strong laws of large numbers and central limit theorem. In the second part of the book, focus is given to Discrete Time Discrete Markov Chains which is addressed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions. A First Course in Probability and Markov Chains: Presents the basic elements of probability. Explores elementary probability with combinatorics, uniform probability, the inclusion-exclusion principle, independence and convergence of random variables. Features applications of Law of Large Numbers. Introduces Bernoulli and Poisson processes as well as discrete and continuous time Markov Chains with discrete states. Includes illustrations and examples throughout, along with solutions to problems featured in this book. The authors present a unified and comprehensive overview of probability and Markov Chains aimed at educating engineers working with probability and statistics as well as advanced undergraduate students in sciences and engineering with a basic background in mathematical analysis and linear algebra.

Supply chain professionals: master pioneering techniques for integrating demand and supply, and create demand forecasts that are far more accurate and useful! In Demand and Supply Integration, Dr. Mark Moon presents the specific design characteristics of a world-class demand forecasting management process, showing how to effectively integrate demand forecasting within a comprehensive Demand and Supply Integration (DSI) process. Writing for supply chain professionals in any business, government agency, or military procurement organization, Moon explains what DSI is, how it differs from approaches such as SandOP, and how to recognize the symptoms of failures to sufficiently integrate demand and supply. He outlines the key characteristics of successful DSI implementations, shows how to approach Demand Forecasting as a management process, and guides you through understanding, selecting, and applying the best available qualitative and quantitative forecasting techniques. You'll learn how to thoroughly reflect market intelligence in your forecasts; measure your forecasting performance; implement state-of-the-art demand forecasting systems; manage Demand Reviews, and much more. For wide audiences of supply chain, logistics, and operations management professionals at all levels, from analyst and manager to Director, Vice President, and Chief Supply Chain Officer; and for researchers and graduate students in the field.

CRUNCH ANY DATA, FROM ANY SOURCE, QUICKLY AND EASILY, WITH EXCEL 2013 PIVOT TABLES! Use Excel 2013 pivot tables and pivot charts to produce powerful, dynamic reports in minutes instead of hours… understand exactly what’s going on in your business… take control, and stay in control! Even if you’ve never created a pivot table before, this book will help you leverage all their amazing flexibility and analytical power. In just the first seven chapters, you learn how to generate complex pivot reports complete with drill-down capabilities and accompanying charts. Then, you go even further, discovering how to build a comprehensive, dynamic pivot table reporting system for any business task or function. Learning advanced pivot table and pivot chart techniques for Excel 2013 or the newest Office 365 has never been easier. You’ll find simple, step-by-step instructions, real-world case studies, even complete, easy recipes for solving your most common business analysis problems. • Create, customize, and change your pivot tables and pivot charts • Transform gigantic data sets into crystal-clear summary reports • Summarize and analyze data even faster with new Excel 2013 recommended pivot tables • Instantly highlight your most (and least) profitable customers, products, or regions • Quickly filter pivot tables using slicers • Use dynamic dashboards using Power View to see exactly where your business stands right now • Revamp analyses on the fly by simply dragging and dropping fields • Build dynamic self-service reporting systems your entire team can use • Use PowerPivot or the Data Model to create pivot tables from multiple data sources and worksheets • Work with and analyze OLAP data, and much more About MrExcel Library: Every book in the MrExcel Library pinpoints a specific set of crucial Excel tasks and presents focused skills and examples for performing them rapidly and effectively. Selected by Bill Jelen, Microsoft Excel MVP and mastermind behind the leading Excel solutions website MrExcel.com, these • Dramatically increase your productivity—saving you 50 hours a year or more • Present proven, creative strategies for solving real-world problems • Show you how to get great results, no matter how much data you have • Help you avoid critical mistakes that even experienced users make CATEGORY: Spreadsheets COVERS: Microsoft Office Excel 2013

Learn to maintain and update scorecards and dashboards with Excel Balanced Scorecards and operational dashboards measure organizational performance and Microsoft Excel is the tool used worldwide to create these scorecards and dashboards. This book covers time-proven step-by-step processes on how to guide executive teams and managers in creating scorecards and dashboards. It then shows Excel developers how to create those scorecards and dashboards. This is the only book that converts theory into practice. The author addresses the people and processes you need to identify strategy and operational metrics and then implement them in dashboards in three versions of Excel. You'll learn how balanced scorecards help organizations translate strategy into action and the ways that performance dashboards enable managers monitor operations. Covers Excel 2010 back to Excel 2003 Shows how to develop consensus on strategy and operational plans with the executive teams Details steps in creating tactical action plans Gives step-by-step guidance in creating the most powerful management dashboards Puts over ten years of experience in one book Balanced Scorecards & Operational Dashboards with Microsoft Excel, Second Edition is the ultimate resource for enhancing your strategic and operational performance.

Probability theory is a rapidly expanding field and is used in many areas of science and technology. Beginning from a basis of abstract analysis, this mathematics book develops the knowledge needed for advanced students to develop a complex understanding of probability. The first part of the book systematically presents concepts and results from analysis before embarking on the study of probability theory. The initial section will also be useful for those interested in topology, measure theory, real analysis and functional analysis. The second part of the book presents the concepts, methodology and fundamental results of probability theory. Exercises are included throughout the text, not just at the end, to teach each concept fully as it is explained, including presentations of interesting extensions of the theory. The complete and detailed nature of the book makes it ideal as a reference book or for self-study in probability and related fields. Covers a wide range of subjects including f-expansions, Fuk-Nagaev inequalities and Markov triples. Provides multiple clearly worked exercises with complete proofs. Guides readers through examples so they can understand and write research papers independently.

Competition is becoming more intense and decision makers are encountering increasing complexity, rapid change, and higher levels of risk. In many situations, the solution is more and better computerized decision support, especially analytics and business intelligence. Today managers need to learn about and understand computerized decision support. If a business is to succeed, managers must know much more about information technology solutions. This second edition of a powerful introductory book is targeted at busy managers and MBA students who need to grasp the basics of computerized decision support, including the following: What are analytics? What is a decision support system? How can managers identify opportunities to create innovative computerized support? Inside, the author addresses these questions and some 60 more fundamental questions that are key to understanding the rapidly changing realm of computerized decision support. In a short period of time, you’ll “get up to speed” on decision support, analytics, and business intelligence.

The fifth edition of Essential MATLAB for Engineers and Scientists provides a concise, balanced overview of MATLAB's functionality that facilitates independent learning, with coverage of both the fundamentals and applications. The essentials of MATLAB are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented clearly and intuitively, along with many examples from a wide range of familiar scientific and engineering areas. This is an ideal book for a first course on MATLAB or for an engineering problem-solving course using MATLAB, as well as a self-learning tutorial for professionals and students expected to learn and apply MATLAB. • Updated with the features of Matlab R2012b • More complete coverage of Matlab windows and menus • Expanded discussion of writing functions and scripts • Revised and expanded Part II: Applications • Expanded section on GUIs • More exercises and examples throughout • Extensive teacher support on http://textbooks.elsevier.com: solutions manual, extra problems, multiple choice questions, PowerPoint slides • Companion website for students providing M-files used within the book and selected solutions to end of chapter problems • Flexible access options: print, e-text, and e-text subscription

The text is for instructors who want to use MATLAB to teach introductory programming concepts. Since many students struggle with applying the concepts that underlie good programming practice, was designed upon the observation that student learning is enhanced if the students themselves build the GUI (graphical user interface) tool, construct the computational model, implement the visualization of results, and design the GUI. This text teaches the core concepts of computer programming—arrays, loops, functions, and basic data structures—using MATLAB. The chapter sequence covers text-based programs, then programs that produce graphics, building up to an emphasis on GUI tools. This progression unleashes the real power of MATLAB—creating visual expressions of the underlying mathematics of a problem or design. Learning to Program with MATLAB: Building GUI Tools

Biostatistics is exclusively designed for the undergraduate students of Botany, Biotechnology and Zoology for gaining fundamental knowledge on biostatistics and its applications. Adequate coverage has been provided to the concepts of biostatistics making this book useful in biological data management.

MATLAB By Example guides the reader through each step of writing MATLAB programs. The book assumes no previous programming experience on the part of the reader, and uses multiple examples in clear language to introduce concepts and practical tools. Straightforward and detailed instructions allow beginners to learn and develop their MATLAB skills quickly. The book consists of ten chapters, discussing in detail the integrated development environment (IDE), scalars, vectors, arrays, adopting structured programming style using functions and recursive functions, control flow, debugging, profiling, and structures. A chapter also describes Symbolic Math Toolbox, teaching readers how to solve algebraic equations, differentiation, integration, differential equations, and Laplace and Fourier transforms. Containing hundreds of examples illustrated using screen shots, hundreds of exercises, and three projects, this book can be used to complement coursework or as a self-study book, and can be used as a textbook in universities, colleges and high schools. No programming experience necessary to learn MATLAB Examples with screenshots and plentiful exercises throughout help make MATLAB easy to understand Projects enable readers to write long MATLAB programs, and take the first step toward being a professional MATLAB programmer

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started. Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: '...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' (The American Statistician, August 2008) 'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' ( Professional Pensions, July 2007)

Have you ever used SAS Enterprise Guide and found yourself wishing that it had that one specific feature, something that you know would make it the perfect tool for your work or industry? You don't have to settle for just the "out of the box" features; you can add your own capabilities with SAS custom tasks!

Chris Hemedinger's new book takes you step-by-step through the process of creating custom tasks for use in SAS Enterprise Guide and SAS Add-In for Microsoft Office. Using standard off-the-shelf development tools for Microsoft .NET, you'll learn how you can hook in your custom processes and make them available to a wide range of SAS users. In the first part of the book, you'll learn how to use the development environment and the programming interfaces provided by SAS to create, test, and deploy new custom tasks. You'll learn about the services that the SAS Enterprise Guide framework offers, including data access, the ability to run SAS programs, and integration of your custom results into a SAS Enterprise Guide project.

In the second part of the book, Hemedinger provides a variety of useful, field-tested and ready-to-run examples—complete with C# and Visual Basic .NET source code. Each example highlights different programming techniques that you can apply immediately within your own custom tasks. The book also addresses important aspects of .NET programming, such as debugging, exception handling, threading models, and user interface design.

This book is part of the SAS Press program.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have written the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards.

Implementing CDISC Using SAS: An End-to-End Guide is an all-inclusive guide on how to implement and analyze Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submissions. Topics covered include creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical.

Anyone dealing with CDISC standards--including SAS or JMP programmers, statisticians, and data managers in the pharmaceutical, biotechnology, or medical device industries--will find the philosophical best practices and implementation examples in this book invaluable.

This book is part of the SAS Press program.

A fresh approach to bridging research design with statistical analysis While good social science requires both research design and statistical analysis, most books treat these two areas separately. Understanding and Applying Research Design introduces an accessible approach to integrating design and statistics, focusing on the processes of posing, testing, and interpreting research questions in the social sciences. The authors analyze real-world data using SPSS software, guiding readers on the overall process of science, focusing on premises, procedures, and designs of social scientific research. Three clearly organized sections move seamlessly from theoretical topics to statistical techniques at the heart of research procedures, and finally, to practical application of research design: Premises of Research introduces the research process and the capabilities of SPSS, with coverage of ethics, Empirical Generalization, and Chi Square and Contingency Table Analysis Procedures of Research explores key quantitative methods in research design including measurement, correlation, regression, and causation Designs of Research outlines various design frameworks, with discussion of survey research, aggregate research, and experiments Throughout the book, SPSS software is used to showcase the discussed techniques, and detailed appendices provide guidance on key statistical procedures and tips for data management. Numerous exercises allow readers to test their comprehension of the presented material, and a related website features additional data sets and SPSS code. Understanding and Applying Research Design is an excellent book for social sciences and education courses on research methods at the upper-undergraduate level. The book is also an insightful reference for professionals who would like to learn how to pose, test, and interpret research questions with confidence.