Scientific Data Analysis by Graham CurrellScientific Data Analysis by Graham Currell

Scientific Data Analysis

byGraham Currell

Paperback | April 1, 2015

Pricing and Purchase Info

$74.44 online 
$82.95 list price save 10%
Earn 372 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


Reliable data analysis lies at the heart of scientific research, helping you to figure out what your data is really telling you. Yet the analysis of data can be a stumbling block for even the most experienced researcher - and can be a particularly daunting prospect when analyzing your own datafor the first time. Drawing on the author's extensive experience, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyze their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.With video content generated by the author to dovetail with the printed text, the resource not only describes the principles of data analysis and the strategies that should be adopted for a successful outcome but also shows you how to carry out that analysis - with the videos breaking down theprocess of analysis into easy-to-follow steps.
Until his retirement in 2009, Graham Currell was a Principal Lecturer in physics at the University of the West of England. During his early career his particular interest was in the preparation of specialist training programmes to support staff in university science laboratories in Asia, the Middle East, Africa and Central America, but...
Title:Scientific Data AnalysisFormat:PaperbackDimensions:352 pages, 9.69 × 7.44 × 0.03 inPublished:April 1, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198712545

ISBN - 13:9780198712541


Table of Contents

Part I - Understanding the Statistics1. Statistical Concepts2. Regression Analysis3. Hypothesis Testing4. Comparing DataPart II - Analysing Experimental Data5. Project Data Analysis6. Single Response Variables7. Related Variables8. Frequency Data9. Multiple Variables