A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.
Patients who undergo TS have high rates of infectious complications. The WBC is not a reliable predictor of infection in these patients in the 2 wks following TS. However, patients who do not develop infection after TS have statistically significantly higher absolute platelet counts and rates of change in their daily platelet counts than those who develop infection.
In this chapter, the method of multiple regression is introduced for describing the functional relationship among several variables, as well as for predicting the values of a variable from the values of a group of variables. The tools for model fitting, model validation, and prediction are presented, while emphasis is given on understanding the types of data that can be analyzed via regression. More specifically, the method of least squares is discussed. Regression analysis is proposed due to its simplicity and wide applicability. Modeling outsourcing or demand forecasting can both be achieved by regression analysis, providing useful information for logistics service providers or 3PL companies. Hauling freight data collected from a logistics company based in Ohio were utilized to demonstrate the applicability of regression analysis and its usefulness for logistics service providers, 3PL companies, and transportation companies. Finally, limitations, solutions, and alternative strategies are discussed.
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