Statistical methods in G-protein-coupled receptor research.
In this chapter we provide an introduction to statistical methods appropriate in G-protein-coupled receptor research, including examples. Topics covered include the choice of appropriate averages and measures of dispersion to summarize data sets, and the choice of tests of significance, including t-tests and one- and two-way analysis of variance (ANOVA) plus posttests for normally distributed (Gaussian) data and their nonparametric equivalents. Techniques for transforming non-normally distributed data to more Gaussian distributions are discussed. Concepts of statistical power, errors, and the use of these in determining the optimal size of experiments are considered. Statistical aspects of linear and nonlinear regression are discussed, including tests for goodness-of-fit to the chosen model and methods for comparing fitted lines and curves.
Freeman
Pat
P
School of Health and Bioscience, University of East London, United Kingdom.
Spina
Domenico
D
D000704:Analysis of Variance; D000818:Animals; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D008954:Models, Biological; D043562:Receptors, G-Protein-Coupled / Q000378:metabolism*; D012044:Regression Analysis; D012459:Salicylates / Q000378:metabolism; D012984:Software; D018709:Statistics, Nonparametric
D043562:Receptors, G-Protein-Coupled; D012459:Salicylates