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Estimating Cost Growth in Engineering and Schedule Cost Categories Using a Two-Pronged Regression Approach54,99 €![Data Analysis and Its Impact on Predicting Schedule and Cost Risk]( "Data Analysis and Its Impact on Predicting Schedule and Cost Risk")
Data Analysis and Its Impact on Predicting Schedule and Cost Risk54,99 €![Predicting the Effect of Longitudinal Variables on Cost and Schedule Performance]( "Predicting the Effect of Longitudinal Variables on Cost and Schedule Performance")
Predicting the Effect of Longitudinal Variables on Cost and Schedule Performance54,99 €![Predicting Cost and Schedule Growth for Military and Civil Space Systems]( "Predicting Cost and Schedule Growth for Military and Civil Space Systems")
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Estimate at Completion: A Regression Approach to Earned Value54,99 €![A Hedonic Approach to Estimating Software Cost Using Ordinary Least Squares Regression and Nominal Attribute Variables]( "A Hedonic Approach to Estimating Software Cost Using Ordinary Least Squares Regression and Nominal Attribute Variables")
A Hedonic Approach to Estimating Software Cost Using Ordinary Least Squares Regression and Nominal Attribute Variables58,99 €![NATO Enlargement: Predicting the Future]( "NATO Enlargement: Predicting the Future")
NATO Enlargement: Predicting the Future54,99 €
Past research shows that schedule growth within the acquisition community is difficult to predict and often adversely affects the cost and performance characteristics within programs. This study describes the use of a two-step procedure for assessing schedule growth of major defense acquisition programs using historical data. Through the operations of both logistic and multiple regression, we seek to predict if a program will experience schedule growth and, if applicable, determine the amount of growth. Past research on cost growth within the EMD phase of acquisition shows favorable results using this two-step process. We extend the use of logistic and multiple regression to the area of schedule growth in acquisition programs. We compile programmatic data from the Selected Acquisition Reports (SARs) between the timeframe of 1990 and 2003. With this methodology, we develop a statistically significant logistic regression model that accurately predicts schedule growth for approximately 89% of the data. For programs with schedule growth, we create a multiple regression model (adjusted R2 of 0.7426) to predict the expected amount of schedule growth.