Binomial Analysis of Recovery and Maintenance Simulation Results - Airland Battle-Future

Abstract

The ordnance center and school uses a simulation model to analyze recovery and maintenance operations on the future battlefield. It runs on a personal computer and is programmed with commercial SLAMSYSTEM software. It simulates an eight hour armored brigade battle in a European scenario. The model is used to evaluate the probable impact of improved recovery vehicles and maintenance vehicles on average repair time needed, recovery time required, and other parameters of interest. It is useful for answering typical what if questions. After completing a run, the model provides data such as the number of tanks available at the end of the battle and at the end of the day. This is count data. The observed counts fall into just two categories, operational or not operational. When this occurs, the data are called binomial data. The investigator's interest is in proportions - the percentage or number of events in one of the two classes. Statistical methods are needed to establish confidence limits on the proportions observed, and to demonstrate significant differences. Mathematically exact methods for analyzing binomial data exist. However, the necessary computations are extremely demanding and time consuming. The use of published binomial tables presents practical difficulties that may lead to inaccuracies in the final results.

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Document Details

Document Type
Technical Report
Publication Date
Nov 14, 1991
Accession Number
ADA245494

Entities

People

  • Robert G. Dick

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Binomials
  • Computations
  • Computer Programs
  • Computers
  • Confidence Limits
  • Data Analysis
  • Data Science
  • Experimental Design
  • Health Care
  • Information Science
  • Personal Computers
  • Probability
  • Probability Distributions
  • Simulations
  • Statistical Analysis
  • Statistical Tests
  • United States

Readers

  • Computational Modeling and Simulation
  • Military History / Militaries and War Studies
  • Regression Analysis.