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Database Modeling from 0 to 60 in 4 Seconds

Database Modeling from 0 to 60 in 4 Seconds
Author: Gavin Powell
Publisher: Createspace Independent Pub
Total Pages: 412
Release: 2012-10-01
Genre: Computers
ISBN: 9781478279990

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This book focuses on the relational database model from the perspective of the data modeling novice, and thus the title Database Modeling from 0 to 60 in 4 Seconds. The objective is to provide an alternative and easy to understand, step-by-step, simple explanation of designing and building relational database models. There are lots of examples and exercises, as well as a multiple chapter case study. People who would benefit from reading this book would be anyone involved with database technology including database administrators, developers, NOVICE data modelers, systems or network administrators, technical managers, marketers, advertisers, forecasters, planners, executives – anyone who doesn't know something about data modeling – and wants to. If You want some kind of clarity to the funny diagrams You see in Your Access database, perhaps built by a programmer, then this book might help You. If You want to know what all that complicated stuff is in the company MySQL, SQL-Server or Oracle database then this book might be a terrific place to start. This book will give enough of an understanding without completely blowing Your mind - and when there are words You've never seen before there is a glossary of terms to explain those words to You. FULL DISCLOSURE: this is a book that is a seriously reworked self-publishing exercise of a book previously printed by a big publisher - this book deserves another attempt. The one thing to remember about this topic is that it's not an exact science and the what and how of data modeling depends upon the application and the circumstances; and I might even tell You sometimes to think about undoing things You've already worked so hard to create and to make Your database perform a little better. So if You're looking for a definitive set of rules You might not like this book. My overall objective in this book is to help people understand data modeling as both a science as well as and an art, by way of tutorial, assuming that after 25 years in the IT field that I might have actually learned a thing or two. This book covers these topics: The History of Database Modeling Databases and Applications The Art of Database Design The Pieces of the Relational Data Model Intuitive Data Modeling and Normalization Reading and Writing Data with SQL Advanced Relational Database Modeling Understanding Data Warehouse Database Modeling Building Faster Performing Database Models Case Study Chapters: Planning and Preparation Creating and Refining Tables Details in Columns and Datatypes Yes this book can be expanded upon in the future but it took about 2 years to get it to this point so let's see how people like this one first.


Data, Models and Analysis

Data, Models and Analysis
Author: Guoqi Han
Publisher: Routledge
Total Pages: 242
Release: 2019-07-09
Genre: Nature
ISBN: 135169121X

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This volume contains the ten most cited articles that have appeared in the journal Atmosphere-Ocean since 1995. These articles cover a wide range of topics in meteorology, climatology and oceanography. Modelling work is represented in five papers, covering global climate model development; a cumulus parameterization scheme for global climate models; development of a regional forecast modelling system and parameterization of peatland hydraulic processes for climate models. Data rehabilitation and compilation in order to support trend analysis work on comprehensive precipitation and temperature data sets is presented in four papers. Field studies are represented by a paper on the circumpolar lead system. While the modelling studies are global in their application and applicability, the data analysis and field study papers cover environments that are specifically, but not uniquely, Canadian. This book will be of interest to researchers, students and professionals in the various sub-fields of meteorology, oceanography and climate science.


Modelling Binary Data, Second Edition

Modelling Binary Data, Second Edition
Author: David Collett
Publisher: CRC Press
Total Pages: 406
Release: 2002-09-25
Genre: Mathematics
ISBN: 1584883243

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Since the original publication of the bestselling Modelling Binary Data, a number of important methodological and computational developments have emerged, accompanied by the steady growth of statistical computing. Mixed models for binary data analysis and procedures that lead to an exact version of logistic regression form valuable additions to the statistician's toolbox, and author Dave Collett has fully updated his popular treatise to incorporate these important advances. Modelling Binary Data, Second Edition now provides an even more comprehensive and practical guide to statistical methods for analyzing binary data. Along with thorough revisions to the original material-now independent of any particular software package- it includes a new chapter introducing mixed models for binary data analysis and another on exact methods for modelling binary data. The author has also added material on modelling ordered categorical data and provides a summary of the leading software packages. All of the data sets used in the book are available for download from the Internet, and the appendices include additional data sets useful as exercises.


Analyzing Data Through Probabilistic Modeling in Statistics

Analyzing Data Through Probabilistic Modeling in Statistics
Author: Jakóbczak, Dariusz Jacek
Publisher: IGI Global
Total Pages: 331
Release: 2021-02-19
Genre: Mathematics
ISBN: 1799847071

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Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic modeling meets statistics, it is a very interesting occasion that has gained much research recently. With the increase of these technologies in life and work, it has become somewhat essential in the workplace to have planning, timetabling, scheduling, decision making, optimization, simulation, data analysis, and risk analysis and process modeling. However, there are still many difficulties and challenges that arrive in these sectors during the process of planning or decision making. There continues to be the need for more research on the impact of such probabilistic modeling with other approaches. Analyzing Data Through Probabilistic Modeling in Statistics is an essential reference source that builds on the available literature in the field of probabilistic modeling, statistics, operational research, planning and scheduling, data extrapolation in decision making, probabilistic interpolation and extrapolation in simulation, stochastic processes, and decision analysis. This text will provide the resources necessary for economics and management sciences and for mathematics and computer sciences. This book is ideal for interested technology developers, decision makers, mathematicians, statisticians and practitioners, stakeholders, researchers, academicians, and students looking to further their research exposure to pertinent topics in operations research and probabilistic modeling.


Hierarchical Modeling and Analysis for Spatial Data, Second Edition

Hierarchical Modeling and Analysis for Spatial Data, Second Edition
Author: Sudipto Banerjee
Publisher: CRC Press
Total Pages: 587
Release: 2014-09-12
Genre: Mathematics
ISBN: 1439819173

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Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. New to the Second Edition New chapter on spatial point patterns developed primarily from a modeling perspective New chapter on big data that shows how the predictive process handles reasonably large datasets New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling New chapter on the theoretical aspects of geostatistical (point-referenced) modeling Greatly expanded chapters on methods for multivariate and spatiotemporal modeling New special topics sections on data fusion/assimilation and spatial analysis for data on extremes Double the number of exercises Many more color figures integrated throughout the text Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages The Only Comprehensive Treatment of the Theory, Methods, and Software This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.


Modelling Survival Data in Medical Research, Second Edition

Modelling Survival Data in Medical Research, Second Edition
Author: David Collett
Publisher: CRC Press
Total Pages: 413
Release: 2003-03-28
Genre: Mathematics
ISBN: 1584883251

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Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.


Applied Modeling Techniques and Data Analysis 2

Applied Modeling Techniques and Data Analysis 2
Author: Yiannis Dimotikalis
Publisher: John Wiley & Sons
Total Pages: 290
Release: 2021-05-11
Genre: Business & Economics
ISBN: 1786306743

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BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.


Models for Discreet Data

Models for Discreet Data
Author: Daniel Zelterman
Publisher: Oxford University Press, USA
Total Pages: 297
Release: 1999-01-21
Genre: Mathematics
ISBN: 0191523437

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Discrete or count data arise in experiments where the outcome variables are the numbers of individuals classified into unique, non-overlapping categories. This revised edition describes the statistical models used in the analysis and summary of such data, and provides a sound introduction to the subject for graduate students and practitioners needing a review of the methodology. With many numerical examples throughout, it includes topics not covered in depth elsewhere, such as thenegative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free models. A detailed treatment of sample size estimation and power are given in terms of both exact inference and asymptotic, non-central chi-squared methods. A new section covering Poissonregression has also been included. An important feature of this book, missing elsewhere, is the integration of the software into the text.Many more exercises are provided (including 84% more applied exercises) than in the previous edition, helping consolidate the reader's understanding of all subjects covered, and making the book highly suitable for use in a classroom setting. Several new datasets, mostly from the health and medical sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.


Dynamic Regression Models for Survival Data

Dynamic Regression Models for Survival Data
Author: Torben Martinussen
Publisher: Springer Science & Business Media
Total Pages: 471
Release: 2007-11-24
Genre: Medical
ISBN: 0387339604

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This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets.