This work describes several statistical techniques for studying repeated measures data, presenting growth curve methods applicable to biomedical, social, animal, agricultural and business research. It details the multivariate development of growth science and repeated measures experiments, covering time-moving covariates, exchangable errors, bioassay results, missing data procedures and nonparametric and Bayesian methods.
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The Growth Curve Model
A Multidimensional Growth Curve Model
The Sum of Profiles and Time Moving
A Growth Curve Model with Exchangeably
Structured Covariance Matrices Model
Growth Curves with Incomplete
algorithm analysis animals Appendix assume assumption Bayesian columns completely randomized design confidence intervals consider correlation covariance matrix covariance structure data matrix data set denote differences DOG(TRT dogs equation error example experimental units F distribution function groups growth curve coefficients growth curve model illustrate independent intra-class correlation Khatri least squares levels likelihood ratio Linear Models MANOVA maximum likelihood estimates MEAN PLASMA SILICON methods missing data multivariate normal multivariate normal distribution nonparametric null hypothesis observations obtained orthogonal Phase polynomial Potthoff-Roy model prediction PROB procedure profiles model pupil sizes rank regression coefficients rejected response sample seemingly unrelated regression simultaneous confidence intervals split-plot Statistic Value Step Study sufficient statistics sum of profiles sums of squares techniques test statistics testing the hypothesis tion treatment effects univariate variables variance variance-covariance matrix vector Wishart distribution Zeger