成本估算模型的数据诊断与处理

提高产品成本估算模型精确度的关键技术是如何进行原始数据的预处理和诊断。对成本原始数据进行了时间价值和学习曲线效应的修正,并采用矩阵的奇异值分解和方差分解比诊断法进行数据的多重共线性诊断,分别采用帽子矩阵法和剔除后的t化残差进行自变量、因变量异常值诊断,用库克距离进行强影响值的诊断,保证了模型所用数据满足要求,提高了模型的精度。

Data diagnosis and processing for cost estimate model

The key technology to improve the precision of the cost estimate model is how to pre-process and diagnose the raw cost data.The raw cost data were modified with the value of time and learning curve effect,and then diagnosed with singular value decomposition and variance decomposition ratio for multicollinearity.Hat matrix and eliminated t-variance were adopted to distinguish the value out of the ordinary from independent variable and dependent variable respectively.The influential cases were discriminated with Cook’s distance.The data were guaranteed to fit the demand of the model,and the precision of this model was improved.