Cover of: Fitting Models to Biological Data Using Linear and Nonlinear Regression | Harvey Motulsky Read Online
Share

Fitting Models to Biological Data Using Linear and Nonlinear Regression A Practical Guide to Curve Fitting by Harvey Motulsky

  • 550 Want to read
  • ·
  • 62 Currently reading

Published by Oxford University Press, USA .
Written in English


Book details:

The Physical Object
Number of Pages384
ID Numbers
Open LibraryOL7391049M
ISBN 100195171799
ISBN 109780195171792

Download Fitting Models to Biological Data Using Linear and Nonlinear Regression

PDF EPUB FB2 MOBI RTF

Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting - Kindle edition by Motulsky, Harvey, Christopoulos, Arthur. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Fitting Models to Biological Data Using Linear and Nonlinear Regression: A 5/5(4). Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting Harvey Motulsky, Arthur Christopoulos Oxford University Press, - Mathematics - . 2. Preparing data for nonlinear regression 3. Nonlinear regression choices 4. The first five questions to ask about nonlinear regression results 5. The results of nonlinear regression 6. Troubleshooting "bad fits" Fitting data with linear regression 7. Choosing linear regression 8. Interpreting the results of linear regression Models 9 Brand: Oxford University Press.   Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting: Authors: Harvey Motulsky, Arthur Christopoulos: Publisher: Oxford University Press, ISBN: , Length: pages: Subjects5/5(1).

FITTING DOSE RESPONSE CURVES An excerpt from a forthcoming book: Fitting models to biological data using linear and nonlinear regression. A practical guide to curve fitting. Harvey Motulsky GraphPad Software Inc. [email protected] Arthur Christopoulos Dept. Pharmacology University of Melbourne [email protected] Size: KB. 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 - Hardcover - Harvey Motulsky; Arthur Christopoulos - Oxford University Press. Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting Harvey Motulsky Arthur Christopoulos Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. COMPARING MODELS AND CURVES An excerpt from a forthcoming book: Fitting models to biological data using linear and nonlinear regression. A practical guide to curve fitting. Harvey Motulsky GraphPad Software Inc. [email protected] Arthur Christopoulos Dept. Pharmacology University of Melbourne [email protected] Size: KB.

  Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting Paperback – Aug. 9 by Harvey Motulsky (Author), Arthur Christopoulos (Author) out of 5 stars 2 ratings. See all 8 formats and editions Hide other formats and 5/5(2). Get this from a library! Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. [Harvey Motulsky; Arthur Christopoulos]. Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. [Harvey Motulsky; Arthur Christopoulos] -- Nonlinear regression is an essential tool for analyzing biological data, and is the most frequently used tool for data analysis in many labs. This book is written for biologists, not. 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.