An algorithm to predict the NDVI (Normalized Difference Vegetation Index) distribution over Mongolia, which is based on a stepwise multiplelinearregression analysis, has been developed using global precipitation dat...
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An algorithm to predict the NDVI (Normalized Difference Vegetation Index) distribution over Mongolia, which is based on a stepwise multiplelinearregression analysis, has been developed using global precipitation data obtained from satellites and global surface air temperature data obtained from the reanalysis data during the period 1998-2005. This algorithm can predict the NDVI value upto 1-3 months in advance for a grid with a spatial resolution of 0.25 degrees x 0.25 degrees. In order to validate the algorithm, the NDVI distribution was predicted for the period from May to November 2006 using 1 to 3-month prediction algorithms. The distributions of the predicted normalized anomalies agreed well with those of the observed normalized anomalies. It was found that these algorithms were effective for and and semi-arid regions, despite its low accuracy for August and regions with high vegetation activity. (C) 2008 Elsevier Ltd. All rights reserved.
An attempt was made to evolve some simple multiple linear regression equations for the prediction of .***2 max max [maximum O2 consumption] from body weight, time for 3.2 km run and exercise dyspnoeic index (DIstd Ex%...
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An attempt was made to evolve some simple multiple linear regression equations for the prediction of .***2 max max [maximum O2 consumption] from body weight, time for 3.2 km run and exercise dyspnoeic index (DIstd Ex%). The predictor variables were selected by examining the product moment correlations of body weight, relative body weight indices, time for 3.2 km run, chest expansion, height and DIstd Ex% with .***2 max, based on data collected on 320 healthy Indian males (17-22 yr). It was observed that body weight, time for 3.2 km run and DIstd Ex% attained maximum correlations with .***2 max. Two regressionequations with 2 and 3 predictor variables were established to predict .***2 max. The 1st yielded a multiple correlation of 0.608 (P < 0.001 SE 0.214 1 .cntdot. min-1]. In this equation, body weight and time for 3.2 km run were considered as significant predictors. To increase the precision of this equation, another multiple linear regression equation based on body weight, time for 3.2 km run and DIstd Ex% as predictors was developed. This equation yielded a multiple correlation of 0.658 (P < 0.001, SE 0.204 l .cntdot. min-1]. Applications of these regressionequations will be of practical importance to biomedical scientists engaged in the development of a simple procedure for indirect assessment of .***2 max, and may serve well as preliminary screening procedures for personnel selection.
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