An Application of Covariance Analysis to Capacity Expansion at an Intermodal Blending Plant

Analysis of covariance (ANCOVA) integrates analysis of variance (ANOVA) and regression. The basic advantages of ANCOVA over ANOVA are: (1) generally greater power, and (2) reduction in bias caused by differences between groups that exist before experimental treatments are administered. ANCOVA has numerous possible applications in the evaluation of simulation output, especially where the values of covariates are not known until after the simulation experiment is completed. These covariates are uncontrolled experimental variables that influence the response but are themselves unaffected by the experimental factors. This paper provides an application of multiple analysis of covariance (MANCOVA) to a simulation experiment to determine whether an intermodal transfer and blending facility should add commodity handling and storage capacity. A discrete simulation model of the plant generated cash flows from several proposed capital projects. These cash flows indicated that capacity expansion was a prudent decision. However, when the treatment means for the various combinations of additional capacity were adjusted by MANCOVA for the same levels of operating volume and scheduling performance, the adjusted cash flows produced unacceptable financial returns. In this example, the increased precision of the MANCOVA model suggested that plant management should not invest in additional storage and commodity handling capacity.     

Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage,Commercial, and Consumer Lending

The concepts of expert systems and decision support systems have received considerable attention recently. While systems have been proposed for various problem areas in business, difficulties still exist in the knowledge acquisition phase of development. This paper presents a recursive partitioning analysis (RPA) approach to knowledge acquisition. The RPA production system approach was applied to data sets representing the mortgage, commercial, and consumer lending problems. Comparison of the classification rates across these problems to the results of a generalized inductive inference production system (Quinlan's ID3 algorithm) and across the mortgage and commercial lending problems to traditional statistical modeling approaches indicated that the RPA approach provided superior results while using fewer variables.     

The Dynamics of Inequality

The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the long‐run income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: “scale dependence” that may arise from changes in skill prices, and “type dependence,” that is, the presence of some “high‐growth types.” These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of “superstar” entrepreneurs or managers.