Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting
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.
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affinity AICe analysis analyze ANOVA antagonist assumption best-fit curve best-fit value binding curve binding data Bmax bottom plateau calculations chapter choose column compare models competitive binding compute concentration of radioligand confidence interval constant value constrain curve fitting data points data sets define degrees of freedom Deming regression determine dialog dissociation dose dose-response curve drug ECso enter enzyme enzyme kinetics equal equation example experimental exponential decay F test fit the data fraction free concentration Gaussian distribution global fitting graph Hill slope HillSlope homologous competition initial values kinetics labeled ligand ligand linear regression logarithm LOGEC50 logECso mean method nonlinear regression program nonspecific binding null hypothesis number of data number of parameters one-site Prism radioactivity radioligand binding rate constant ratio receptor replicates response Scatchard plot scatter ſº specific binding standard curve standard errors statistical substrate sum-of-squares total binding two-site unlabeled variable Vmax zero