Author: Rand CorporationPublisher:
ISBN:
Category :
Languages : en
Pages : 100
Book Description
BENCHMARK SEPARATION PROJECT METHOD FOR PREDICTING MONTHLY LOSSES OF AIR FORCE ENLISTED PERSONNEL.
Author: Rand CorporationThe Benchmark Separation Projection Method for Predicting Monthly Losses of Air Force Enlisted Personnel
Author: C. Peter RydellPublisher:
ISBN:
Category : United States
Languages : en
Pages : 136
Book Description
RAND is helping to design an Enlisted Force Management System (EFMS) for the Air Force. The efms is a decision support system designed to assist managers of the enlisted force in setting and meeting force targets. The system contains computer models that project the force resulting from given management actions, so actions that meet targets can be found. Some of those models analyze separate job specialties (disaggregate models) and others analyze the total enlisted force across all specialties (aggregate models); some models make annual projections (middle-term models) and others monthly projections. The Short-Term Aggregate Inventory Projection Model (SAM) is the component of the EFMS that makes monthly projections (for the rest of the current fiscal year) of the aggregate enlisted force.
Time Series Models for Predicting Monthly Losses of Air Force Enlisted Personnel
Author: Marygail K. BraunerPublisher:
ISBN:
Category : United States
Languages : en
Pages : 108
Book Description
The Robust Separation Projection Method for Predicting Monthly Losses of Air Force Enlisted Personnel
Author: Marygail K. BraunerPublisher:
ISBN:
Category : United States
Languages : en
Pages : 74
Book Description
Short-term Aggregate Model for Projecting Air Force Enlisted Personnel (SAM)
Author: C. Peter RydellPublisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 168
Book Description
Predicting Involuntary Separation of Enlisted Personnel
Author: Walter G. AlbertPublisher:
ISBN:
Category : Prediction of occupational success
Languages : en
Pages : 2
Book Description
This report contains the results of a study to compare the classification accuracy of the Motivational Attrition Prediction (MAP) method to the classification accuracy of other statistical algorithms capable of predicting involuntary separation within the Air Force enlisted force. The MAP computer program, which was implemented on the UNIVAC 1108 computer system at the Air Force Human Resources Laboratory, was modified to increase its data-handling and computational capabilities and was thoroughly tested. This report includes a description of the computerized statistical algorithms, subsample selection from the first-term airman population, independent and dependent variables, model formulation and analysis, comparison of required computer resources, and related research efforts. (Author).
Government Reports Annual Index
Author: Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1836
Book Description
Government Reports Announcements & Index
Author: Forecasting Officer Losses
Author: T. M. BeattyPublisher:
ISBN:
Category : Employee retention
Languages : en
Pages : 21
Book Description
Air Force Personnel Managers must be able to accurately forecast the force size. This need is explicit in meeting statutory budget limitations. Further officer losses drive accession, training, and promotion, thus the need for accuracy in forecasting losses cannot be over emphasized. To accomplish this objective loss rates have been generated using Ordinary Least Squares (OLS) stepwise regression. The objective of this paper is to expose the relative efficacies of alternative methods which could be used viz. Maximum Likelihood Estimation (MLE) and OLS standardized coefficient (Beta) predictor models. (Author).
Developing an Air Force Retention Early Warning System
Author: David SchulkerPublisher:
ISBN: 9781977407474
Category : Business & Economics
Languages : en
Pages : 0
Book Description
RAND Project Air Force was tasked with developing a new capability for planners: a retention early warning system (REWS) that alerts policymakers when a subgroup of U.S. Air Force (USAF) military members is at risk for future shortages. The goal of the research project was to develop a forecasting model for retention, operationalized within a prototype decision-support application, that can alert decisionmakers to emerging problems and thus allow them enough time to consider adjusting accession and retention policies before shortages occur. The authors' overall approach to designing the system drew on widely used paradigms for solving data science problems. These paradigms emphasize understanding the business problem, drawing on a wide array of data sources and types, testing several flexible prediction approaches to optimize performance, and operationalizing the information for decisionmaking. To gain an understanding of the data sources that would be desirable for this application, the authors performed an extensive review of the turnover literature and identified gaps in existing USAF data collection efforts.