Long-Term Forest Health Monitoring Program in the Eastern Mountains and Rivers Network Evaluation of the Statistical Power to Detect Temporal Trends - Brossura
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We evaluated the power of the ERMN Forest and Soil Monitoring Program’s sampling design to detect trends in 30 key forest health variables. We used a simulation approach to examine the statistical power to detect temporal trends in 17 non-count variables and four count variables using the variance components estimated from mixed models. We were unable to perform power analyses for nine of the count variables because mixed models failed to converge (i.e., estimates of variance components were not achieved).
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Long-Term Forest Health Monitoring Program in the Eastern Mountains and Rivers Network Evaluation of the Statistical Power to Detect Temporal Trends
Editore:
CreateSpace Independent Publishing Platform, 2013
ISBN 10: 1491297751
ISBN 13: 9781491297759
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Brossura
Da: California Books, Miami, FL, U.S.A.
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Condizione: New. Print on Demand. Codice articolo I-9781491297759
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Long-Term Forest Health Monitoring Program in the Eastern Mountains and Rivers Network Evaluation of the Statistical Power to Detect Temporal Trends
Editore:
Createspace Independent Publishing Platform, 2013
ISBN 10: 1491297751
ISBN 13: 9781491297759
Nuovo
Paperback / softback
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. Codice articolo C9781491297759
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Long-Term Forest Health Monitoring Program in the Eastern Mountains and Rivers Network Evaluation of the Statistical Power to Detect Temporal Trends (Paperback)
Editore:
Createspace Independent Publishing Platform, 2013
ISBN 10: 1491297751
ISBN 13: 9781491297759
Nuovo
Paperback
Da: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. We evaluated the power of the ERMN Forest and Soil Monitoring Program's sampling design to detect trends in 30 key forest health variables. We used a simulation approach to examine the statistical power to detect temporal trends in 17 non-count variables and four count variables using the variance components estimated from mixed models. We were unable to perform power analyses for nine of the count variables because mixed models failed to converge (i.e., estimates of variance components were not achieved). This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781491297759
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