Exploratory Modeling and Analysis PDF Download
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Author: Buyung Agusdinata
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages : 308
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Book Description
Author: Buyung Agusdinata
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages : 308
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Book Description
Author: Steven C. Bankes
Publisher:
ISBN:
Category : Computer simulation
Languages : en
Pages : 30
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Book Description
Author: Rand Corporation
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Steven C. Bankes
Publisher:
ISBN:
Category : Policy sciences
Languages : en
Pages : 15
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Book Description
Exploratory modeling is using computational experiments to assist in reasoning about systems where there is significant uncertainty. While frequently confused with the use of models to consolidate knowledge into a package that is used to predict system behavior, exploratory modeling is a very different kind of use, requiring a different methodology for model development. This paper distinguishes these two broad classes of model use, describes some of the approaches used in exploratory modeling, and suggests some technological innovations needed to facilitate it.
Author: Michel Jambu
Publisher: Elsevier
ISBN: 0080923674
Category : Mathematics
Languages : en
Pages : 489
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Book Description
With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques. - Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones - Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples - Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques
Author: Steven C. Bankes
Publisher:
ISBN:
Category : Policy sciences
Languages : en
Pages : 6
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Book Description
Exploratory modeling uses computational experiments with computer models to inform questions of interest. Exploratory modeling is primarily useful for situations where insufficient information exists to build a veridical model of the system of interest. The problem of how to cleverly select the finite sample of models and cases to examine from the infinite set of possibilities is the central problem of exploratory modeling methodology. Thus, in exploratory modeling, rather than validate models, one must validate research strategies. This validation centers on three aspects of the analysis: the specification of an ensemble of models that will be the basis for exploration, the strategy for sampling from this ensemble, and the logic used to connect experimental results to study conclusions. The ability of researchers to achieve useful results in this paradigm can be greatly enhanced by the syntactic definition of compound computational experiments resulting in the automatic generation of large numbers of individual experiments, and tools supporting the visualization of these results.
Author: Ronald K. Pearson
Publisher: CRC Press
ISBN: 0429847033
Category : Business & Economics
Languages : en
Pages : 548
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Book Description
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
Author: David B. Farrar
Publisher:
ISBN:
Category :
Languages : en
Pages : 260
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Book Description
Author: Marley Watkins
Publisher: Routledge
ISBN: 1000336565
Category : Psychology
Languages : en
Pages : 199
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Book Description
This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code, and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.
Author: MIT Critical Data
Publisher: Springer
ISBN: 3319437429
Category : Medical
Languages : en
Pages : 435
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Book Description
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
