Multiple regression analysis was used to examine scores on the Pharmacy College Admission Test (PCAT) and pre-pharmacy grade point averaaes (GPAs) as predictors of academic success in pharmacy school. Separate analyses were made for White (n=471), Black (n=54), and Asian (n=96) subgroups, as well

Comparative modeling of CO2 laser cutting using

Summary: Non-specificities and interferences may become complex when they involve the analyte as well as other interfering substances. These non-specificities and interferences are known as analyte-dependent and multi-interferent interferences. Multiple regression analysis has proven valuable

Multiple regression analysis is the most powerful tool that is widely used, but also is one of the most abused statistical techniques (Mendenhall and Sincich 339). There are assumptions that need to be satisfied, statistical tests to determine the goodness fit of the data and accuracy of the model

Kukuk and Baty (1979) have been critical of the use of factor scores as predictors in multiple regression analysis. The present paper, while in disagreement with certain of Kukuk and Baty’s conclusions, also finds the perfunctory use of factor scores in conjunction with regression analysis

Long-term global reanalysis of atmospheric observations generates a high-quality homogeneous climate data set using a fixed modern data assimilation system and quality-controlled comprehensive observations. Re-analysis products have been used for a wide variety of climate studies and applications

affects network performance. The relay network performance is also influenced by various factors, such as background noise level, path loss exponent, density of relay nodes, and transmission signal strength. In this paper, we present the results of multiple regression analysis of IEEE 802.16j relay

multiple regression and the

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a neural network