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Spatial variations in stream FC concentrations can be predicted to a first approximation using multiple regression based on catchment characteristics.
Nonetheless, results from the multiple regression, based on a smaller sample (just 334 pairs for teacher-rated achievement and 1646 pairs for test achievement), were highly similar, with twin correlations of 0.52 and 0.35 for MZ and DZ twins for teacher-rated achievement, and 0.47 and 0.27 for MZ and DZ twins for test performance.
We imputed missing values using multiple regression based on the assumption that missing values are random.
A power analysis suggested we needed about 40 subjects for the multiple regression based on a recommendation of 10 patients per variable included.
We see that compared to both HyperLasso regression and multiple regression based on univariate variable selection, our method has higher TPR and lower FPR at all values of the threshold.
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Data extracted from these maps for 27 headwater catchments of the Ontonagon River in northern Michigan, USA were used with DOC concentrations measured in catchment streams to develop stepwise multiple regressions based on wetland area and type.
Only 6 districts exhibited significant multiple regressions based on both variables.
Each explanatory variable was first entered alone (univariate analysis), followed by multiple regressions based on several variables.
Such test results were shown to be superior compared to those obtained from a multiple-regression based model and a slab area base-unit analysis model.
We then propose a new method called multiple instance regression based scoring (MIRS) that incorporates unbound ligand conformations using multiple scoring functions.
Hence, new prediction models are proposed through multiple linear regression based on the YD SCPTU database, which relies on CPT-based variables (qt, fs, Bq, Ic, z) and additional laboratory data.
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