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® 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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