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Estimating the Error in Model Predictions

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Statistical procedures are considered for the problem of determining how well a model performs in predicting the values of variables that are observed in a real system of interest. A careful development of the assumptions underlying a procedure proposed by Freese is presented, along with some extensions and alternate interpretations of this procedure. The uses and interpretations of confidence intervals, prediction intervals, and tolerance intervals for the error involved in using a model are discussed. Two examples using stochastic stand simulation models for predicting stand basal areas are also given. Forest Sci. 30:454-469.

Keywords: Critical error; Freese's chi-squared test; model validation; prediction interval; simulation model; stand simulator; tolerance interval

Document Type: Journal Article

Affiliations: Associate Professor of Forestry and Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

Publication date: 1984-06-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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