Thomas R. Gulledge

Use of the Correlation Coefficient with Normal Probability Plots

Abstract The use of the correlation coefficient is suggested as a technique for summarizing and objectively evaluating the information contained in probability plots. Goodness-of-fit tests are constructed using this technique for several commonly used plotting positions for the normal distribution. Empirical sampling methods are used to construct the null distribution for these tests, which are then compared on the basis of power against certain nonnormal alternatives. Commonly used regression tests of fit are also included in the comparisons. The results indicate that use of the plotting position pi = (i - .375)/(n + .25) yields a competitive regression test of fit for normality.

Production rate, learning, and program costs: Survey and bibliography

Abstract This survey contains items that we have found useful in our integrative research on production rate, learning, and cost. The references given relate to aggregate planning, assembly line balancing, and overhead redistribution. The survey is narrow in that it only relates to learning augmented production models that contain production rate as a decision variable. General references to the learning curve are not included. We make no claims that this survey is complete. Our efforts have concentrated on the economics, industrial engineering, and operations research literature. The authors solicit omitted items for inclusion in future revisions.

A generalization of the learning curve

Abstract This paper presents a generalization of models that relate required resources to accumulated experience. These learning curve models are often used as planning tools in made-to-order production programs. This is especially true in the acquisition of major defense items. However, learning curves are deficient in that they only consider a single cost determinant, cumulative experience. The model presented in this paper considers both cumulative experience and production rate as cost determinants. As a decision application it is shown how the model may be used to generate the type of program cost data that is used in planning for made-to-order programs.

An application of multiattribute utility theory to the planning of emergency medical services

This research considers the problem of relating Emergency Medical Services (EMS) to patient outcome. The hypothesis is that response time alone may be misleading as an EMS performance criterion. This research uses methods for approximating multiattribute utility functions to consider both response time and on-the-scene care. The final result is an optimization problem where the response time and desired personnel requirements are decision variables. These are important inputs in the planning for Emergency Medical Services.

Regression Tests of Fit and Probability Plotting Positions

Probability plots are commonly used as a technique for testing distributional assumptions. However, any conclusion about the linearity of such a plot is based strictly on the users judgment. Regression tests of fit are supposed to make this procedure more objective, but these tests typically are not based on probability plots as they are constructed in practice. This is because the developers of these tests defined probability plots in terms of plotting positions which are not used by practitioners. In this paper, a class of goodness-of-fit test statistics which are calculated directly from probability plots as they are constructed in practice is described. Several realistic plotting positions for the normal distribution are chosen and empirical sampling methods are used to derive the null distribution of each of the corresponding test statistics. These tests are then compared on the basis of 5% power against certain nonnormal alternatives. Results of the comparisons indicate that the test based on the p...

Production Rate and Contractor Behavior

Abstract There has been much interest in recent years in the relationships among learning, production rate, and program costs. These relationships are of particular interest in military acquisitions research where, because of the nature of the funding process, the government must assess the cost impact of numerous production rate changes. One approach that is often used to analyze the problem is an empirical application of the “Alchian cost function.” Department of Defense analysts routinely use these models for planning purposes. The purpose of this paper is to demonstrate that the models as being applied imply nonsensical contractor behavior.

Subjective evaluation of composite econometric policy inputs

Abstract Studies have suggested that a composite forecast may be preferred to a single forecast. In addition, forecasting objectives are often conflicting. For example, one forecast may have the smallest sum of absolute forecast errors, while another has the smallest maximum absolute error. This paper examines the appropriateness of using multiple objective linear programming to determine weighted linear combinations of forecasts to be used as inputs for policy analysis. An example is presented where the methodology is used to combine the forecasts for several policy variables. The forecasts are selected from large econometric, consensus, and univariate time series models.

Learning curves and production functions: An integration

This paper explores a class of dynamic cost models that are designed for made-to-order production and are particularly applicable to the airframe industry. In these models we use optimal control theory to describe the time paths of resource use and production rate. The analytic solution for the most general specification of the model is unknown, however the imposition of a restriction on the relation between production rate and experience rate leads to a straightforward solution. The model implies that there exists an optimal trade-off between learning and production rate during the life of a program and that production rate should be changing throughout the life of the program in order to minimize cost. The model is compared with the learning curve, and the applicability of the model is explored by performing various parametric sensitivity analyses. The models behavior is consistent with the dynamics of actual made-to-order production programs.

Learning and production costs: An application of a cost prediction model to a fighter airframe program☆

Abstract This research reports on the application of a cost prediction model to a fighter airframe program. The model considers the effects of learning and production rate changes on discounted program cost. The results indicate that this application compares favorably with those of other programs reported previously.