Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document.  Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University.  Both authors have presented numerous R workshops to forestry professionals and scientists, and others.

• Introduction.- Forest data management.- Data analysis for common inventory methods.- Imputation and Interpolation.- Fitting dimensional distributions.- Linear and non-linear models.- Fitting linear hierarchical models.- Simulations.- Forest estate planning and optimization.

From the reviews: "The material presented in this text is more than sufficient for a dedicated module of an applied statistics course ... . The authors develop, and demonstrate, solutions to common forestry data handling and analysis challenges ... . Whilst much of the text may be regarded as standard for the topic, the last chapter addresses an area harvest strategy which is well worth reading on its own ... . The text is well written, easy to read and I recommend it to anyone interested in biometrics." (Carl M. O'Brien, International Statistical Review, Vol. 80 (1), 2012)

Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document.  Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University.  Both authors have presented numerous R workshops to forestry professionals and scientists, and others.

• Introduction.- Forest data management.- Data analysis for common inventory methods.- Imputation and Interpolation.- Fitting dimensional distributions.- Linear and non-linear models.- Fitting linear hierarchical models.- Simulations.- Forest estate planning and optimization.

From the reviews: "The material presented in this text is more than sufficient for a dedicated module of an applied statistics course ... . The authors develop, and demonstrate, solutions to common forestry data handling and analysis challenges ... . Whilst much of the text may be regarded as standard for the topic, the last chapter addresses an area harvest strategy which is well worth reading on its own ... . The text is well written, easy to read and I recommend it to anyone interested in biometrics." (Carl M. O'Brien, International Statistical Review, Vol. 80 (1), 2012)

Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document.  Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University.  Both authors have presented numerous R workshops to forestry professionals and scientists, and others.

• Introduction.- Forest data management.- Data analysis for common inventory methods.- Imputation and Interpolation.- Fitting dimensional distributions.- Linear and non-linear models.- Fitting linear hierarchical models.- Simulations.- Forest estate planning and optimization.

From the reviews: "The material presented in this text is more than sufficient for a dedicated module of an applied statistics course ... . The authors develop, and demonstrate, solutions to common forestry data handling and analysis challenges ... . Whilst much of the text may be regarded as standard for the topic, the last chapter addresses an area harvest strategy which is well worth reading on its own ... . The text is well written, easy to read and I recommend it to anyone interested in biometrics." (Carl M. O'Brien, International Statistical Review, Vol. 80 (1), 2012)