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- W2504218241 abstract "Software Cost Estimation is a critical phase in the development of a software project, and over the years has become an emerging research area. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. The purpose of this chapter is to show how a combination of modern statistical and computational techniques can be used to compare the effect of missing data techniques on the accuracy of cost estimation. Specifically, a recently proposed missing data technique, the multinomial logistic regression, is evaluated and compared with four older methods: listwise deletion, mean imputation, expectation maximization and regression imputation with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error and the comparisons are conducted using statistical tests, resampling techniques and a visualization tool, the regression error characteristic curves." @default.
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- W2504218241 date "2011-05-25" @default.
- W2504218241 modified "2023-09-27" @default.
- W2504218241 title "Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation" @default.
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- W2504218241 doi "https://doi.org/10.4018/978-1-60960-215-4.ch009" @default.
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