Filetype Pdf Survivorship Curves Microsoft Excel


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Microsoft Azure Essentials Azure Machine Learning


Microsoft Azure Essentials Azure Machine Learning

Author: Jeff Barnes

language: en

Publisher: Microsoft Press

Release Date: 2015-04-25


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Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.

Spreadsheet Exercises in Ecology and Evolution


Spreadsheet Exercises in Ecology and Evolution

Author: Therese Marie Donovan

language: en

Publisher: Sinauer Associates Incorporated

Release Date: 2002


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The exercises in this unique book allow students to use spreadsheet programs such as Microsoftr Excel to create working population models. The book contains basic spreadsheet exercises that explicate the concepts of statistical distributions, hypothesis testing and power, sampling techniques, and Leslie matrices. It contains exercises for modeling such crucial factors as population growth, life histories, reproductive success, demographic stochasticity, Hardy-Weinberg equilibrium, metapopulation dynamics, predator-prey interactions (Lotka-Volterra models), and many others. Building models using these exercises gives students "hands-on" information about what parameters are important in each model, how different parameters relate to each other, and how changing the parameters affects outcomes. The "mystery" of the mathematics dissolves as the spreadsheets produce tangible graphic results. Each exercise grew from hands-on use in the authors' classrooms. Each begins with a list of objectives, background information that includes standard mathematical formulae, and annotated step-by-step instructions for using this information to create a working model. Students then examine how changing the parameters affects model outcomes and, through a set of guided questions, are challenged to develop their models further. In the process, they become proficient with many of the functions available on spreadsheet programs and learn to write and use complex but useful macros. Spreadsheet Exercises in Ecology and Evolution can be used independently as the basis of a course in quantitative ecology and its applications or as an invaluable supplement to undergraduate textbooks in ecology, population biology, evolution, and population genetics.

Data Analysis Using SQL and Excel


Data Analysis Using SQL and Excel

Author: Gordon S. Linoff

language: en

Publisher: John Wiley & Sons

Release Date: 2010-09-16


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Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.