Applying Data Science

Applying Data Science

Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions.

Author: Gerhard Svolba

Publisher: SAS Institute

ISBN: 9781635260540

Category: Computers

Page: 490

View: 653

See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.
Categories: Computers

Applied Analytics through Case Studies Using SAS and R

Applied Analytics through Case Studies Using SAS and R

This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data.

Author: Deepti Gupta

Publisher: Apress

ISBN: 9781484235256

Category: Computers

Page: 404

View: 576

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.
Categories: Computers

Learn Business Analytics in Six Steps Using SAS and R

Learn Business Analytics in Six Steps Using SAS and R

Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partners need to make better decisions faster.

Author: Subhashini Sharma Tripathi

Publisher: Apress

ISBN: 9781484210017

Category: Computers

Page: 219

View: 842

Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partners need to make better decisions faster. Learn Business Analytics in Six Steps Using SAS and R teaches you how to solve problems and execute projects through the "DCOVA and I" (Define, Collect, Organize, Visualize, Analyze, and Insights) process. You no longer need to choose between the two most popular software tools. This book puts the best of both worlds—SAS and R—at your fingertips to solve a myriad of problems, whether relating to data science, finance, web usage, product development, or any other business discipline. What You'll Learn Use the DCOVA and I process: Define, Collect, Organize, Visualize, Analyze and Insights. Harness both SAS and R, the star analytics technologies in the industry Use various tools to solve significant business challenges Understand how the tools relate to business analytics See seven case studies for hands-on practice Who This Book Is For This book is for all IT professionals, especially data analysts, as well as anyone who Likes to solve business problems and is good with logical thinking and numbers Wants to enter the analytics world and is looking for a structured book to reach that goal Is currently working on SAS , R, or any other analytics software and strives to use its full power
Categories: Computers

Applying Data Science

Applying Data Science

In most cases the available time series forecasting methods are unreliable over a
long time span. ... Modern Forecasting Approaches Some AI-based Data Science
methods can overcome the shortcomings of the classical forecasting approaches
discribed above. ... such as SVMs, genetic programming, and neural networks
have been used for forecasting in academic studies. ... 9See details in T. Rey, A.
Kordon, and C. Wells, Applied Data Mining for Forecasting, SAS Press, 2012.

Author: Arthur K. Kordon

Publisher: Springer Nature

ISBN: 9783030363758

Category:

Page:

View: 697

Categories:

Applied Data Mining for Business and Industry

Applied Data Mining for Business and Industry

This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a ...

Author: Paolo Giudici

Publisher: John Wiley & Sons

ISBN: 9780470058862

Category: Computers

Page: 249

View: 296

This new edition sees the inclusion of 70% new material, including eight new case studies, that brings this best selling title up to date with the many advances made in the field since its original publication. In the text all the methods described are either computational or of a statistical modelling nature; complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of both students and industry professionals.
Categories: Computers

Data Mining for Business Analytics

Data Mining for Business Analytics

19.6 Collecting Social Network Data with Python In this section, we briefly show
how to collect data from one of the ... It requires pre-registration as a developer, to
obtain an application authorization code. ... Output Text Mining and Analysis:
Practical Methods, Examples, and Case Studies Using SAS https://t.co/
jFjmmSgjt0 ...

Author: Galit Shmueli

Publisher: John Wiley & Sons

ISBN: 9781119549857

Category: Mathematics

Page: 608

View: 141

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Categories: Mathematics

Analytics in a Big Data World

Analytics in a Big Data World

With this book, the author provides a unique blend of research and business
insights into data science and/or ... focuses on case studies, real-world
application, and steps for implementation, using theory and mathematical
formulas only when ...

Author: Bart Baesens

Publisher: John Wiley & Sons

ISBN: 9781118892749

Category: Business & Economics

Page: 256

View: 824

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.
Categories: Business & Economics

Business Analytics Principles Concepts and Applications with SAS

Business Analytics Principles  Concepts  and Applications with SAS

Chapter objectives: • List and describe the commonly used prescriptive analytics
in the business analytics (BA) process. • Explain the role of case studies in
prescriptive analytics. ... Step 3 of the BA process, prescriptive analytics, involves
the application of decision science, management science, or operations ...
Explain the value of linear programming in the prescriptive analytics step Data-
Driven Additional Models Possible Applications Information Simulation This
methodology can.

Author: Marc J. Schniederjans

Publisher: Pearson Education

ISBN: 9780133989571

Category: Computers

Page: 99998

View: 795

Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications with SAS brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, this text demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself. Business Analytics Principles, Concepts, and Applications with SAS will be a valuable resource for all beginning-to-intermediate level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.
Categories: Computers

Multiple Imputation of Missing Data Using SAS

Multiple Imputation of Missing Data Using SAS

Case/control study observations will be stratified by an outcome and will attempt
to establish association of the outcome with prior exposures and risk factors. ... for
applying MI to data from randomized trials (Taylor and Zhou 2009), randomized
cluster trials (Andridge and Little 2011), ... and analytical studies in the fields of
social science, education, health, agriculture, natural resources, and business.

Author: Patricia Berglund

Publisher: SAS Institute

ISBN: 9781629592039

Category: Computers

Page: 164

View: 406

Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.
Categories: Computers

A User s Guide to Business Analytics

A User s Guide to Business Analytics

The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book.

Author: Ayanendranath Basu

Publisher: CRC Press

ISBN: 9781466591660

Category: Business & Economics

Page: 400

View: 567

A User's Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book. The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random variables, and probability distributions, with a number of business scenario examples. These chapters lay down the foundation for predictive analytics and model building. Chapter eight deals with statistical inference and discusses the most common testing procedures. Chapters nine through twelve deal entirely with predictive analytics. The chapter on regression is quite extensive, dealing with model development and model complexity from a user’s perspective. A short chapter on tree-based methods puts forth the main application areas succinctly. The chapter on data mining is a good introduction to the most common machine learning algorithms. The last chapter highlights the role of different time series models in analytics. In all the chapters, the authors showcase a number of examples and case studies and provide guidelines to users in the analytics field.
Categories: Business & Economics

Data Analytics Applications in Latin America and Emerging Economies

Data Analytics Applications in Latin America and Emerging Economies

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors.

Author: Eduardo Rodriguez

Publisher: CRC Press

ISBN: 9781351673167

Category: Business & Economics

Page: 272

View: 902

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.
Categories: Business & Economics

Data Science for Business and Decision Making

Data Science for Business and Decision Making

As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work.

Author: Luiz Paulo Fávero

Publisher: Academic Press

ISBN: 0128112166

Category: Business & Economics

Page: 1000

View: 861

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs
Categories: Business & Economics

Applied Data Mining

Applied Data Mining

This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice.

Author: Paolo Giudici

Publisher: John Wiley & Sons

ISBN: 9780470871393

Category: Computers

Page: 376

View: 345

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems. Provides a solid introduction to applied data mining methods in a consistent statistical framework Includes coverage of classical, multivariate and Bayesian statistical methodology Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real life applications Features a number of detailed case studies based on applied projects within industry Incorporates discussion on software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional material Includes an extensive bibliography and pointers to further reading within the text Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.
Categories: Computers

Human Capital Analytics

Human Capital Analytics

This visionary book will inspire you to apply the same tools used to evaluate and improve finance, marketing, and operations investments to human capital investments, with the same rigor.

Author: Gene Pease

Publisher: John Wiley & Sons

ISBN: 9781118466766

Category: Business & Economics

Page: 236

View: 685

Human capital analytics, also known as human resources analytics or talent analytics, is the application of sophisticated data mining and business analytics techniques to human resources data. Human Capital Analytics provides an in-depth look at the science of human capital analytics, giving practical examples from case studies of companies applying analytics to their people decisions and providing a framework for using predictive analytics to optimize human capital investments.
Categories: Business & Economics

Power Query

Power Query

Power Query ist ein in Excel integriertes Tool, mit dem man Daten aus verschiedenen Quellen extrahieren kann, z.

Author: Gil Raviv

Publisher:

ISBN: OCLC:1151009375

Category:

Page: 456

View: 947

Power Query ist ein in Excel integriertes Tool, mit dem man Daten aus verschiedenen Quellen extrahieren kann, z. B. csv- und xls-Dateien, Access-, SAP- oder SQL-Datenbanken oder Statistiken aus dem Internet. Diese Daten kann man so transformieren, dass sie der Struktur der Zieldatenbank entspricht, und sie schließlich in diese laden. PowerBI ist eine Sammlung von Microsoft-Tools zur Analyse und Visualisierung von Daten. Dieses Buch erklärt fortgeschrittenen Excel-Nutzern und Datenanalysten, wie sie mit Power Query in Power BI und Excel:- Daten für Analysen einfach vorbereiten (importieren, bereinigen, aufarbeiten, umarbeiten)- Daten aus verschiedenen Quellen kombinieren und anpassen- Datensätze zusammenführen und vergleichen- Aufgaben automatisieren.
Categories:

Text as Data

Text as Data

Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and ...

Author: Barry DeVille

Publisher: Wiley

ISBN: 1119487129

Category: Computers

Page: 256

View: 141

Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field. Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analytics—and the underlying principles at work—and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.
Categories: Computers

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data ...

Author: Robert Nisbet

Publisher: Academic Press

ISBN: 0080912036

Category: Mathematics

Page: 864

View: 514

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book
Categories: Mathematics

INFORMS Conference Program

INFORMS Conference Program

SD13 M – ROOm 8216 Data and Text Mining Applications in Finance, CRM and
Security Sponsor. Data Mining SOOnSOred Session Chair: Mary Crissey, SAS
Institute, 17030 Vista Park Dr, San Antonio, TX, 78.247, United States of America,
Mary Grace. ... 3 - Predictive Analytics Applied to Public Safety and Security
Colleen McCue, Senior Research Associate, Security ... 2 - Service Six Sigma (
Case Studies) Fugee Tsung, Professor, Hong Kong University of Science and
Technology, ...

Author: Institute for Operations Research and the Management Sciences. National Meeting

Publisher:

ISBN: CORNELL:31924083389233

Category: Industrial management

Page:

View: 989

Categories: Industrial management

SAS for Epidemiologists

SAS for Epidemiologists

Cavalieri P, Marovich P, Patetta MJ, Walsh S, Bond C, SAS Institute (2000)
Statistics I: Introduction to anova, regression, and logistic regression: course
notes. SAS Institute, Cary, NC 2. Daniel WW (2006) Biostatistics: a foundation for
analysis in the health sciences 8th edition with SPSS software CD Rom ...
Hosmer DW, Lemeshow S (2000) Applied logistic regression (Wiley series in
probability and statistics). ... Schlesselman JJ (1982) Case-control studies:
design, conduct, analysis.

Author: Charles DiMaggio

Publisher: Springer Science & Business Media

ISBN: 9781461448549

Category: Mathematics

Page: 258

View: 479

This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a “hands on” approach to the use of SAS for a broad number of epidemiologic analyses, readers learn techniques for data entry and cleaning, categorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps for successful programming. SAS (Statistical Analysis System) is an integrated system of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engage in many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widely used in the fields of epidemiology and public health research, predominately due to its ability to reliably analyze very large administrative data sets, as well as more commonly encountered clinical trial and observational research data.
Categories: Mathematics

Applied Regression Modeling

Applied Regression Modeling

The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work. Praise for the First Edition "The attention to detail is impressive.

Author: Iain Pardoe

Publisher: John Wiley & Sons

ISBN: 9781118345047

Category: Mathematics

Page: 346

View: 231

Praise for the First Edition "The attention to detail is impressive. The book is very wellwritten and the author is extremely careful with his descriptions .. . the examples are wonderful." —The AmericanStatistician Fully revised to reflect the latest methodologies and emergingapplications, Applied Regression Modeling, Second Editioncontinues to highlight the benefits of statistical methods,specifically regression analysis and modeling, for understanding,analyzing, and interpreting multivariate data in business, science,and social science applications. The author utilizes a bounty of real-life examples, casestudies, illustrations, and graphics to introduce readers to theworld of regression analysis using various software packages,including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear andcareful writing style, the book introduces modeling extensions thatillustrate more advanced regression techniques, including logisticregression, Poisson regression, discrete choice models, multilevelmodels, and Bayesian modeling. In addition, the Second Edition features clarificationand expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated andadditional problems are included at the end of each chapter,allowing readers to test their comprehension of the presentedmaterial. In addition, a related website features the book'sdatasets, presentation slides, detailed statistical softwareinstructions, and learning resources including additional problemsand instructional videos. With an intuitive approach that is not heavy on mathematicaldetail, Applied Regression Modeling, Second Edition is anexcellent book for courses on statistical regression analysis atthe upper-undergraduate and graduate level. The book also serves asa valuable resource for professionals and researchers who utilizestatistical methods for decision-making in their everyday work.
Categories: Mathematics