Customer Story Modernizing the National Bank of Greece with SAS Viya on Azure Leading Greek financial institution pursues digital transformation backed by advanced analytics to become the bank of choice for businesses and private individuals. E-Book Governments leading through change In this e-book, SAS has collected the wisdom and stories of the government agencies that are rising to the challenges of our times and applying analytics to drive evidence-based decision making.
These stories are pulled from every sector of government and are relevant across all sectors because they each share innovative new ways to respond to a changing landscape.
Series Tracking down answers to your questions about data scientists Do you need a data scientist? Want to be a data scientist, or improve the skills you already have? Check out our Insights series. E-Book Resilience: The best way to beat coronavirus To be truly resilient means proactively seeking ways to ensure operational and financial stability no matter the circumstances. Customer Story Hands-on analytics training prepares students for real world SAS powers analytics lab at the University of Alabama to connect students with potential employers.
Customer Story Building customer satisfaction with better network monitoring SAS Analytics helps a major telecom organization analyze traffic, anticipate problems and improve customer service. E-Book The new analytics ecosystem Many organizations are struggling to evolve their current analytics ecosystem, with disparate groups working across multiple technologies and tools.
We surveyed more than 1, organizations to determine their level of adoption of technology to evolve their analytics ecosystem.
Article Inside sports analytics: Ten lessons for business leaders Post "Moneyball" many sports teams take a highly analytical approach to winning, and there are several important lessons that businesses can learn. These 10 tips come from research Thomas H. Davenport conducted on more than 25 professional teams. Webinar Starting Your Cloud Analytics Journey Take your analytics skills to new heights with this introduction to cloud analytics.
Learn directly from expert Manish Jain of Stratacent. Customer Story Advanced analytics empowers the Government of Odisha to combat pandemic and help keep citizens safe Indian state uses COVID dashboard to track cases, allocate medical resources and prepare for future crises.
We will take a look at the criteria and techniques used for model selection. Article Bringing data to the stream How much do we know about fresh water systems and the dynamic nature of streams and rivers? Find out how one data scientist turned his fascination with streams and rivers into a career. Article How to drill a better hole with analytics From drilling holes to preventing health care fraud, learn about some of the new technologies SAS has patented with IoT and machine learning technologies.
Customer Story How to arm graduate students with real-world skills SAS aids Oklahoma State University in educating the business analysts of the future. Article 5 ways to measure beehive health with analytics and hive-streaming data This analytical approach to understanding bee hive health can automatically alert beekeepers to changes in hive weights, temperatures, flight activity and more.
Article Saving lives during a global pandemic through medical resource optimization Cleveland Clinic is operationalizing analytics to combat COVID, creating innovative models that help forecast patient volume, bed capacity, medical equipment availability and more. Article How health care leaders deployed analytics when crisis hit During the COVID pandemic, some health care providers were well-positioned to respond to rapid changes in demand. The factor that most distinguished them was that they already had a strong capacity in place for using data to inform decisions.
Read about three key takeaways from their experiences. E-Book Building your data and analytics strategy Launch successful data analytics projects, improve data prep and go beyond conventional data governance. Read on to help your organization become truly data-driven. Customer Story Analytics provides valuable and trustworthy insights for FinTechs to seize future opportunities SAS helps Nova Credit efficiently develop core business bureau scorecards and models, while minimizing lending risk and improving governance.
Join seasoned data scientists as they discuss lessons they've learned and skills they've perfected. Customer Story Smart data exploration advances K public education programs The South Carolina Department of Education depends on SAS to analyze data and properly fund and serve its school districts.
Webinar Get an integrated analytics platform for your midsized company in weeks with SAS Unified Insights Experts from IDC, Core Compete and SAS will discuss best practices for the effective use of analytics and demo a fast, affordable and complete cloud deployment solution for midsize organizations. Customer Story Turn unstructured content into business value Creditreform uses text analytics to turn unstructured content into valuable business insights.
Customer Story Healthier lives for our four-legged friends Agria uses SAS to provide better care for pets and peace of mind for their owners. E-Book Your data scientist hiring guide This ebook provides a brief guide for data scientist hiring.
It offers 20 interview questions, 3 data scientist profiles and tips on building a data science program. The book provides a call-to-action to the data scientist primer page. Webinar Analytics — Part 2 Join us for a deeper dive into analytics and learn how to use AI and machine learning techniques to gain meaningful insights from unstructured text.
E-Book Out in the open with analytics Learn how businesses in multiple industries are combining the benefits of SAS and open source analytics, integrating disparate code and information to deploy models and deliver critical results. Customer Story Improving patient care and reducing costs with visual analytics Gelderse Vallei Hospital brings data analysis directly to medical staff. Article Five questions to help bridge the analytics gap Everyone is turning to analytics for a competitive edge.
But the real advantage is in using analytics as a tool for exploration and innovation. Article Big data in government: How data and analytics power public programs Big data generated by government and private sources coupled with analytics has become a crucial component for a lot of public-sector work.
Because using analytics can improve outcomes of public programs. Webinar Causal Inferences: An Introduction Data analysts and statisticians can learn how causal inference helps explain whether results can be attributed to a given cause in varied situations. Article Optimizing well placement to eliminate water poverty How data visualization is helping Water for Good bring fresh water to the Central African Republic.
Article From lab to life Once you've created your analytical model, you need to put it to use. Here are tips from finance industry experts to get your models in the hands of users.
And in these unpredictable times, it is important to remain resilient and be prepared to bounce back. This article explores what it truly means to be resilient, how to build it, and how analytics can help you act when your resilience is tested. Customer Story Community outreach and support for COPD patients enhanced through natural language processing and machine learning Text analytics and sentiment analysis from SAS help the COPD Foundation provide health resources and emotional support for patients and caregivers.
Learn how to handle missing values in your data. Customer Story Building a culture of analytics empowers university to lead as an educational enterprise From enrollment to graduation, the University of Texas at Arlington uses SAS Analytics for Education on Amazon Web Services to aid in analysis supporting student success and campus strategic decision making.
The COVID pandemic heightened this need, as leaders across industries used analytics and visualization to gain real-time situational awareness and respond with fast, critical decisions. Webinar Building the Foundation for Analytic Success Learn about organizational best practices for analytics and what it takes to build a thriving analytics culture.
Customer Story Analytics turns service repair data into cost savings American Honda turns to SAS to help make sense of warranty and service data. Article Public health infrastructure desperately needs modernization Public health agencies must flex to longitudinal health crises and acute emergencies — from natural disasters like hurricanes to events like a pandemic.
To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization. Customer Story Predictive analytics empowers bank to act quickly with confidence OTP Bank Romania can better assess risk, meet profitability targets and enhance customer satisfaction through a streamlined data mining process from SAS. Customer Story Improving loss ratios and profitability Triad Analytic Solutions helps insurers benefit from advanced analytics.
Customer Story Curbing traffic accidents and saving lives with machine learning Artificial intelligence and cloud computing make roads safer in Western Australia. Webinar How Top Brands Are Embracing Next-Gen Markdown Optimization Learn how top retailers are using new technology to make intelligent decisions and automation seamless across their data.
E-Book White Paper Your guide to bridging the analytics skills gap This e-book pulls together five customer interviews to explain where analytics skills are needed in different industries from journalism to insurance to government. The customers explain how to provide training and cultural changes to support analytics careers.
Customer Story Using artificial intelligence to decode dance patterns of bees Amesto NextBridge and Beefutures use visual analytics and machine learning to help protect and support healthy bee populations. This webinar is for data scientists, data engineers, data stewards and analysts. Learn how to avoid common pitfalls when using ODS Graphics procedures. Webinar Optimizing Manufacturing Quality and Yield With SAS Analytics Join us to learn how SAS advanced analytics can help manufacturers identify leading causes of unwanted variation and recommend optimal set points to maximize quality.
Customer Story Solving crime in less time with analytics Delaware State Police use law enforcement solutions from SAS to help identify suspects, crack cases and protect communities. Article Preventing domestic violence with wearables After her sister became a victim of domestic violence, Kimberly Calhoun developed a wearable that collects data and reports on the offender's location in real-time, alerting police and protecting victims.
Customer Story Municipality puts wireless water meter-reading data to work Town of Cary, North Carolina uses SAS Analytics to analyze data from wireless water meters, assess demand, detect problems and engage customers. E-Book Innovation: From data to business Report on findings from a global expert interview study on the topic of analytics driven innovation; Peer insights to help navigate the journey from data to innovation.
Article Three steps for conquering the last mile of analytics Putting your analytical models into production can be the most difficult part of the analytics journey. It is a pathway to growth and better service. Matching up location information with big and little data can revolutionize business practices. Learn more in this e-book. Customer Story Transforming steelmaking through IoT analytics SSAB improves production efficiency, product quality and maintenance strategies using sensor data, artificial intelligence and advanced analytics.
Article The transformational power of evidence-based decision making in health policy State health agencies are under pressure to deliver better health outcomes while minimizing costs. Springer, Collection Pratique R, 1st edition, Les commandes permettant le traitement des exemples sous le logiciel R figurent dans le corps du texte. R Cookbook. O'Reilly, first edition, Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master.
R Cookbook will help both beginners and experienced statistical programmers unlock and use the power of R. O'Reilly, Each solution gives you just what you need to know to get started with R for basic statistics, graphics, and regression.
These solutions were selected from O'Reilly's R Cookbook, which contains more than recipes for R. R Graphics, Second Edition. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graphics. The book reviews the graphics facilities of the R language and describes R's powerful grid graphics system.
It then covers the graphics engine, which represents a common set of fundamental graphics facilities, and provides a series of brief overviews of the major areas of application for R graphics and the major extensions of R graphics.
Mathematical Statistics with Resampling and R. Wiley, 1st edition, This book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.
The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis, Calculation of sampling distributions, The Central Limit Theorem, Monte Carlo sampling, Maximum likelihood estimation and properties of estimators, Confidence intervals and hypothesis tests, Regression, Bayesian methods.
Case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the material. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels.
It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work. An R Companion to Applied Regression. It introduces R, and concentrates on how to use linear and generalized-linear models in R while assuming familiarity with the statistical methodology. R Graphs Cookbook. Packt Publishing, This practical guide begins by teaching you how to make basic graphs in R and progresses through subsequent dedicated chapters about each graph type in depth.
It will demystify a lot of difficult and confusing R functions and parameters and enable you to construct and modify data graphics to suit your analysis, presentation, and publication needs.
Data Mining with Rattle and R: The art of excavating data for knowledge discovery. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining.
With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software.
The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment.
The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. Numerical Methods and Optimization in Finance.
Academic Press, It covers fundamental numerical analysis and computational techniques, for example for option pricing, but two topics are given special attention: simulation and optimization. Many chapters are organized as case studies, dealing with problems like portfolio insurance or risk estimation; in particular, several chapters explain optimization heuristics and how to use them for portfolio selection or the calibration of option pricing models.
Such practical examples allow readers to learn the required steps for solving specific problems, and to apply these steps to other problems, too. At the same time, the chosen applications are relevant enough to make the book a useful reference on how to handle given problems. Analysis of Questionnaire Data with R. As with written material, there are almost always differences between the meaning of the original text and translated text.
Additionally, many versions can be suggested, each with their advantages and disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations.
As indicated in the title, the syntax of these statistical formulations is based on the well-known R language, chosen for its popularity, simplicity, and power of its structure. Although syntax is vital, understanding the semantics is the real challenge of any good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand.
Sometimes data sets contain errors, inconsistencies between answers, or missing data. More often, available statistical tools are not formally appropriate for the given situation, making it difficult to assess to what extent this slight inadequacy affects the interpretation of results. Analysis of Questionnaire Data with R tackles these and other common challenges in the practice of statistics. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone.
The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures ADTs and parallel computing concepts. The translation of a mathematical problem into its computational analog or analogs is a skill that must be learned, like any other, by actively solving relevant problems.
The book lays the foundation for original code development in a research environment. The R Primer. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. Rather than explore the many options available for every command as well as the ever-increasing number of packages, the book focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point.
The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated.
While base R is used throughout, other functions or packages are listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R.
Additional topics and R code are available from the book's supporting website at www. Robert and George Casella. Springer, 1st edition, Ce livre adopte donc le point de vue du programmeur pour exposer ces outils fondamentaux de simulation stochastique. Tous les chapitres incluent des exercices. R Companion to Linear Models.
For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.
This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates.
It also demonstrates applications of several pre-packaged functions for complex computational procedures. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.
The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models MGLMMs using either standard or adaptive Gaussian quadrature.
The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples.
In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on.
As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided the freely available programming language R is used throughout.
Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics.
Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US. Muenchen and Joseph M. R for Stata Users. Statistics and Computing. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.
R in Action. Manning, The book begins by introducing the R language, including the development environment. As you work through various examples illustrating R's features, you'll also get a crash course in practical statistics, including basic and advanced models for normal and non- normal data, longitudinal and survival data, and a wide variety of multivariate methods.
Both data mining methodologies and approaches to messy and incomplete data are included. Statistiques avec R.
Didact Statistiques. Presses Universitaires de Rennes, 2nd edition, Le Logiciel R. R in a Nutshell [deutsche Ausgabe]. Along the way, you will learn how to use R to arrive at practical solutions and how to effectively communicate your results.
Ultimately, the fate of the kingdom depends on your ability to make informed, data- driven decisions with R. Computer Sciences and Data Analysis. It covers principal component analysis PCA when variables are quantitative, correspondence analysis CA and multiple correspondence analysis MCA when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables.
Within this framework, they present the principles, indicators, and ways of representing and visualizing objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical.
They also illustrate the methods and the ways they can be exploited using examples from various fields. Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.
Statistics and Data Analysis for Financial Engineering. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration.
The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful. Introducing Monte Carlo Methods with R. Use R. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems.
Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella Similarly, the more exploratory and less stable solutions are not covered here.
This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis-Hastings and Gibbs algorithms, and adaptive algorithms.
All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more.
The programming parts are introduced progressively to be accessible to any reader. Clinical Trial Data Analysis with R. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R.
The book's practical, detailed approach draws on the authors' 30 years of real-world experience in biostatistical research and clinical development. Each chapter presents examples of clinical trials based on the authors' actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified.
The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data.
With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.
Spatial Statistics and Modeling. Springer Series in Statistics. This book covers the best-known spatial models for three types of spatial data: geostatistical data stationarity, intrinsic models, variograms, spatial regression and space-time models , areal data Gibbs-Markov fields and spatial auto-regression and point pattern data Poisson, Cox, Gibbs and Markov point processes. The level is relatively advanced, and the presentation concise but complete.
The most important statistical methods and their asymptotic properties are described, including estimation in geostatistics, autocorrelation and second-order statistics, maximum likelihood methods, approximate inference using the pseudo-likelihood or Monte-Carlo simulations, statistics for point processes and Bayesian hierarchical models.
A large number of real examples are studied with R, and each chapter ends with a set of theoretical and applied exercises. While a foundation in probability and mathematical statistics is assumed, three appendices introduce some necessary background.
The book is accessible to senior undergraduate students with a solid math background and Ph. Furthermore, experienced statisticians and researchers in the above-mentioned fields will find the book valuable as a mathematically sound reference. Robinson and Jeff D. Forest Analytics with R.
The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling.
The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics.
Vinod, editor. Lecture Notes in Statistics. The following twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software. McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime.
Koenker: Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition. Gelman: R graphics in the context of voter participation in US elections.
Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment. Markus and Gu: New R tools for exploratory data analysis including bubble plots.
Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts, relevant to anyone with an investment portfolio. Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation.
Their job search illustration is of interest during current times of high unemployment. Haupt, Schnurbus, and Tschernig: consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation. Rindskopf: R methods to fit a multinomial based multivariate analysis of variance ANOVA with examples from psychology, sociology, political science, and medicine.
Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. Numatsi and Rengifo: explain persistent discrete jumps in financial series subject to misspecification. It introduces tools to enable readers to learn and use fundamental methods for constructing quantitative models of biological mechanisms, both deterministic and with some elements of randomness, including complex reaction equilibria and kinetics, population models, and regulation of metabolism and development; to understand how concepts of probability can help in explaining important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data from spectroscopic, genomic, and proteomic sources.
These quantitative tools are implemented using the free, open source software program R. R provides an excellent environment for general numerical and statistical computing and graphics, with capabilities similar to Matlab. Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers [ 76 ] Uwe Ligges. Programmieren mit R. Springer-Verlag, Heidelberg, 3rd edition, Generalised Linear Models in R].
Biologie dnes. Scientia, Praha, Je urcena predevsim studentum a kolegum z biologickych oboru a vyzaduje pouze zakladni statisticke vzdelani, jakym je napr.
Text knihy obsahuje nezbytne minimum statisticke teorie, predevsim vsak reseni 18 realnych prikladu z oblasti biologie. Kazdy priklad je rozpracovan od popisu a stanoveni cile pres vyvoj statistickeho modelu az po zaver.
K analyze dat je pouzit popularni a volne dostupny statisticky software R. Priklady byly zamerne vybrany tak, aby upozornily na lecktere problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout.
Zaroven maji ctenare motivovat k tomu, jak o statistickych modelech premyslet a jak je pouzivat. Reseni prikladu si muse ctenar vyzkouset sam na datech, jez jsou dodavana spolu s knihou. Springer Series in Statistics and Computing. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches.
The programs and practice datasets are available for download. Heiberger and Erich Neuwirth. R Through Excel. The presentation is designed as a computational supplement to introductory statistics texts. The authors provide RExcel examples for most topics in the introductory course. Data can be transferred from Excel to R and back. The clickable RExcel menu supplements the powerful R command language. Results from the analyses in R can be returned to the spreadsheet. Ordinary formulas in spreadsheet cells can use functions written in R.
The book is accessible to readers with only a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. R code is provided throughout the text.
Cowpertwait and Andrew Metcalfe. Introductory Time Series with R. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the R routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set. By using R, the whole procedure can be reproduced by the reader.
The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyze time series as part of their taught program or their research. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.
Henry H. A Primer of Ecology with R. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology.
Graduate students in ecology, along with upper division undergraduates and faculty, will all find this to be a useful overview of important topics. Introduction to Multivariate Statistical Analysis in Chemometrics. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects both the practical approach of chemometrics and the more formally oriented one of statistics.
To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts. There are different ways to achieve that.
If you want to turn your R program into an executable, you can specify that you want the file to run using Rscript by adding the following line at the beginning of your R script. There is no one best way to learn how to program using the R programming language.
Depending on your learning style, you can choose between any of the resources available online. At DataMentor, we have created a ton of resources to help you get started with learning R. You can use our tutorials to get started with statistics using R. We cover how to. It is not a comprehensive guide to the R programming language, but an excellent primer to get started with the language. If you are a software engineer looking to build software in R, this is the book for you. On the other hand, the book might not be ideal if you are a statistician trying to learn programming through R.
An excellent book for beginners who want to learn the notoriously complex R concepts through clear simple statistical examples. To get help on specific topics, we can use the help function along with the topic we want to search. We can also use the? We also have the help. We could use the?? You must be itching to start learning R by now. Our collection of R tutorials will help you learn R. Whether you are a beginner or an expert, each tutorial explains the relevant concepts and syntax with easy-to-understand examples.
Learn and get help from others. There are tons of great R communities that will help you solve real-life problems and become better in R. With the advent of IoT devices creating terabytes and terabytes of data that can be used to make better decisions, data science is a field that has no other way to go but up.
Simply explained, a data scientist is a statistician with an extra asset: computer programming skills. Programming languages like R give a data scientist superpowers that allow them to collect data in realtime, perform statistical and predictive analysis, create visualizations and communicate actionable results to stakeholders. Statistical computing R is the most popular programming language among statisticians. Discover new patterns. Create rich visuals and share insights. With our easy-to-use analytics and business intelligence tools, you can:.
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With SAS, you can:. Yes, it is possible to satisfy both business users who want to create their own BI content and IT who wants to selectively manage and govern it. Spend lots of time in MS Office?
Why not add BI and analytics? The world is built on teamwork. So why not put business intelligence and analytics where they can add more value? It's here. It's there. It's everywhere. Get powerful insights and facts wherever and whenever they're needed. This TDWI Checklist Report reveals seven steps for applying a balanced governance strategy when expanding your use of self-service visual analytics and discovery.
Interactive Demos. Free Trial. View interactive demos. Limitless BI tools. Visual Data Exploration Seeing is knowing. Overview Why SAS? How to Buy Explore all your data. With our easy-to-use analytics and business intelligence tools, you can: Get the picture. Quickly gain insights using automated analysis backed by machine learning, with easy-to-understand natural language explanations. Visually explore all relevant data, and quickly spot hidden relationships. See it. Understand it. Compelling visuals help you quickly grasp what the data has to tell you.
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