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.