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SAS for R users : a book for budding data scientists
\"This book will enable students and practitioners to easily switch from R to SAS and vice versa. R has better statistical and graphical tools, while SAS has faster data handling, is easier to learn and is the leading corporate software in analytics. This book builds a cross-functional framework for students who already know R but may need to work on SAS language in corporate environments. Using a simple how-to-do-it in a step-by-step way approach, the book presents an analytics workflow similar to those used by the everyday data scientist. The book is designed to be compatible with the latest R packages as well as SAS University Edition. It also includes advanced section for the reader who wishes to get a greater understanding of more advanced methods. The book will be useful to students, researchers, and practitioners globally as well as anyone looking to get a job in data science today\"-- Provided by publisher.
Book
Ultimate Statistical Analysis System (SAS) for Data Analytics
The \"Ultimate Statistical Analysis System (SAS) for Data Analytics\" is your go-to resource for mastering SAS, a powerful software suite for statistical analysis. This comprehensive book covers everything from the basics of SAS for data professionals to advanced topics like clustering analysis and association rules. With practical examples and clear explanations, this book equips readers with the knowledge and skills needed to excel in their roles as data scientists, analysts, researchers, and more.Whether you're a beginner looking to build a solid foundation in SAS or an experienced user seeking to expand your proficiency, this handbook has something for everyone. You'll learn essential techniques for importing, cleaning, and visualizing data, as well as conducting hypothesis testing, regression analysis, and inferential statistics. Advanced topics like SAS programming concepts and generating reports are also covered in detail, providing readers with the tools to tackle complex data challenges with confidence.With its accessible writing style and emphasis on real-world applications, this book is a practical guide that empowers readers to unlock the full potential of their data. Whether you're analyzing customer behavior, optimizing business processes, or conducting academic research, this handbook will be your trusted companion on the journey to mastering SAS and making informed decisions based on data-driven insights.
eBook
Learn business analytics in six steps using SAS and R : a practical, step-by-step guide to learning business analytics
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. You will: 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.
Book
Statistical hypothesis testing with SAS and R
A comprehensive guide to statistical hypothesis testing with examples in SAS and R
When analyzing datasets the following questions often arise:
Is there a short hand procedure for a statistical test available in SAS or R?
If so, how do I use it?
If not, how do I program the test myself?
This book answers these questions and provides an overview of the most common
statistical test problems in a comprehensive way, making it easy to find and perform
an appropriate statistical test.
A general summary of statistical test theory is presented, along with a basic
description for each test, including the necessary prerequisites, assumptions, the
formal test problem and the test statistic. Examples in both SAS and R are provided,
along with program code to perform the test, resulting output and remarks
explaining the necessary program parameters.
Key features:
• Provides examples in both SAS and R for each test presented.
• Looks at the most common statistical tests, displayed in a clear and easy to follow way.
• Supported by a supplementary website http://www.d-taeger.de [http://www.d-taeger.de/] featuring example
program code.
Academics, practitioners and SAS and R programmers will find this book a valuable
resource. Students using SAS and R will also find it an excellent choice for reference
and data analysis.
eBook
Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
Here readers can 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.
Book
Applied Regression and ANOVA Using SAS
Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps.
Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided.
Features:
Statistical concepts presented in words without matrix algebra and calculus
Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics
Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection
Suggestions of alternative approaches when a method's ideal inference conditions are unreasonable for one's data
This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.
eBook
SAS for R Users
BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHERSAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS.Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the otherProvides command-by-command translations of R to SAS and SAS to ROffers examples and applications in both R and SASPresents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and moreIncludes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics.AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.
eBook
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. 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 LearnUse 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 challengesUnderstand how the tools relate to business analytics See seven case studies for hands-on practiceWho This Book Is ForThis book is for all IT professionals, especially data analysts, as well as anyone whoLikes 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 goalIs currently working on SAS , R, or any other analytics software and strives to use its full power
eBook
Supervised Machine Learning
The artificial intelligence (AI) framework is intended to solve a the problem of bias--variance tradeoff for supervised learning methodsin real-life applications. The AI framework It comprises of bootstrapping to create multiple training and testing datasetsdata sets with various characteristics, design, and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for machine learning (ML) methods, and data contamination to test for the robustness of the classifiers.
Key Features:
Using ML methods by itself doesn't does notensure building classifiers that generalize well for new data.
Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using the design and analysis of statistical experiments.
Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias.
Developing of anSAS-based table-driven environment allows managing the management of all meta-data related to the proposed AI framework and creating the creation of interoperability with R libraries to accomplish a variety of statistical and machine-learning tasks.
Computer programs in R and SAS that create AI frameworks are available on GitHub.
eBook