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Fitting Models to Biological Data Using Linear and Nonlinear Regression

2004-05-27 Mathematics
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Release : 2004-05-27
Genre : Mathematics
Kind : eBook
Book Rating : 348/5 ( reviews)

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Book Synopsis Fitting Models to Biological Data Using Linear and Nonlinear Regression by : Harvey Motulsky

Download or read book Fitting Models to Biological Data Using Linear and Nonlinear Regression written by Harvey Motulsky. This book was released on 2004-05-27. Available in PDF, EPUB and Kindle. Book excerpt: Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Fitting Models to Biological Data Using Linear and Nonlinear Regression

2004-05-27 Mathematics
Download Fitting Models to Biological Data Using Linear and Nonlinear Regression PDF Online Free

Author :
Release : 2004-05-27
Genre : Mathematics
Kind : eBook
Book Rating : 344/5 ( reviews)

GET EBOOK


Book Synopsis Fitting Models to Biological Data Using Linear and Nonlinear Regression by : Harvey Motulsky

Download or read book Fitting Models to Biological Data Using Linear and Nonlinear Regression written by Harvey Motulsky. This book was released on 2004-05-27. Available in PDF, EPUB and Kindle. Book excerpt: Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Nonlinear Regression with R

2008-12-11 Mathematics
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Release : 2008-12-11
Genre : Mathematics
Kind : eBook
Book Rating : 167/5 ( reviews)

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Book Synopsis Nonlinear Regression with R by : Christian Ritz

Download or read book Nonlinear Regression with R written by Christian Ritz. This book was released on 2008-12-11. Available in PDF, EPUB and Kindle. Book excerpt: - Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.

Applied Statistics in Agricultural, Biological, and Environmental Sciences

2020-01-22 Technology & Engineering
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Release : 2020-01-22
Genre : Technology & Engineering
Kind : eBook
Book Rating : 590/5 ( reviews)

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Book Synopsis Applied Statistics in Agricultural, Biological, and Environmental Sciences by : Barry Glaz

Download or read book Applied Statistics in Agricultural, Biological, and Environmental Sciences written by Barry Glaz. This book was released on 2020-01-22. Available in PDF, EPUB and Kindle. Book excerpt: Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Applied Linear Statistical Models

2005 Mathematics
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Release : 2005
Genre : Mathematics
Kind : eBook
Book Rating : 882/5 ( reviews)

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Book Synopsis Applied Linear Statistical Models by : Michael H. Kutner

Download or read book Applied Linear Statistical Models written by Michael H. Kutner. This book was released on 2005. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

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