Currell G. Scientific Data Analysis

Oxford: Oxford University Press, 2015. — 335 p. — ISBN: 978-0-19-871254-1.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 data for the first time.
Drawing on the author's extensive experience of supporting project students, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse 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 the process of analysis into easy-to-digest chunks.
With guidance on the use of Minitab, SPSS and Excel, Scientific Data Analysis doesn't just support the use of one particular software package: it is the ideal guide to carrying out your own data analysis regardless of the software you have chosen.
Online Resource Centre:
The Online Resource Centre to accompany the book features over 80 video screencasts that walk the viewer step-by-step through the techniques and approaches outlined in the book.Understanding the Statistics
Statistical conceptsData visualization
Scientific data
Dala distributions
Uncertainty and error
Sample data
Hypothesis tests
Regression analysisRegression statistics
Experimental uncertainties
Linearization techniques
Iteration using Solver
Hypothesis testingt-tests and z-tests
Analysis of variance
Multiple factots ANOVA
General linear motiel
Nonparametric analyses
Repeated measurements
Chi-squared analyses
Frequency and proportions
Resampling techniques
Comparing dateCorrelation
Tests tor association
Strength of association
Agreement between variables
Analysing experimental data
Project data analysis
Intfoduction
Preparing datafor analysis
Deriving test characteristics
Transforming and weighting data
Normality and homoscedasticity
Single response variableOne sample
Two samples
One factor
Multiple factors and interactions
Related variablesRegression, correlation and agreement
Nonlinear relationships
General x-y data
Frequency dataSingle variable
Contingency tables
Binary output data
Multiple variablesModelling multiple variables
Multiple questions
Appendix I. Videos available in the Online Resource Centre
Appendix II Case studies used throughout This book