Growth Curve Modeling - by Michael J Panik (Hardcover)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Features recent trends and advances in the theory and techniques used to accurately measure and model growth</b></p> <p><i>Growth Curve Modeling: Theory and Applications</i> 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.</p> <p>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, <i>Growth Curve Modeling: Theory and Applications</i> also features: </p> <p>- Statistical distribution analysis as it pertains to growth modeling<br /> - Trend estimations<br /> - Dynamic site equations obtained from growth models<br /> - Nonlinear regression<br /> - Yield-density curves<br /> - Nonlinear mixed effects models for repeated measurements data</p> <p><i>Growth Curve Modeling: Theory and Applications</i> 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.</p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>Features recent trends and advances in the theory and techniques used to accurately measure and model growth</b> <p><i>Growth Curve Modeling: Theory and Applications</i> 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. <p>SAS<sup>(R)</sup> 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, <i>Growth Curve Modeling: Theory and Applications</i> also features: <ul> <li>Statistical distribution analysis as it pertains to growth modeling</li> <li>Trend estimations</li> <li>Dynamic site equations obtained from growth models</li> <li>Nonlinear regression</li> <li>Yield-density curves</li> <li>Nonlinear mixed effects models for repeated measurements data</li> </ul> <p><i>Growth Curve Modeling: Theory and Applications</i> 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.<p/><br></br><p><b> Review Quotes </b></p></br></br><br><p>"Thus, it 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." (<i>Zentralblatt MATH</i>, 1 April 2015)</p> <p> </p><br><p/><br></br><p><b> About the Author </b></p></br></br><p><b>MICHAEL J. PANIK, PHD, </b>is Professor Emeritus in the Department of Economics at the University of Hartford. He has served as a consultant to the Connecticut Department of Motor Vehicles as well as to a variety of healthcare organizations. In addition, Dr. Panik is the author of numerous books and journal articles in the areas of economics, mathematics, and applied econometrics.
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