Features recent trends and advances in the theory and techniques
used to accurately measure and model growth Growth Curve Modeling:
Theory and Applications features an accessible introduction to
growth curve modeling and addresses how to monitor the change in
variables over time since there is no one size fits all approach to
growth measurement. A review of the requisite mathematics for
growth modeling and the statistical techniques needed for
estimating growth models are provided, and an overview of popular
growth curves, such as linear, logarithmic, reciprocal, logistic,
Gompertz, Weibull, negative exponential, and log-logistic, among
others, is included. In addition, the book discusses key
application areas including economic, plant, population, forest,
and firm growth and is suitable as a resource for assessing recent
growth modeling trends in the medical field. SAS(R) is utilized
throughout to analyze and model growth curves, aiding readers in
estimating specialized growth rates and curves. Including
derivations of virtually all of the major growth curves and models,
Growth Curve Modeling: Theory and Applications also features:
Statistical distribution analysis as it pertains to growth modeling
Trend estimations Dynamic site equations obtained from growth
models Nonlinear regression Yield-density curves Nonlinear mixed
effects models for repeated measurements data Growth Curve
Modeling: Theory and Applications is an excellent resource for
statisticians, public health analysts, biologists, botanists,
economists, and demographers who require a modern review of
statistical methods for modeling growth curves and analyzing
longitudinal data. The book is also useful for upper-undergraduate
and graduate courses on growth modeling.
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