文章基本信息

标题：Data Quality in Linear Regression Models: Effect of Errors in Test Data and Errors in Training Data on Predictive Accuracy
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作者：Barbara D. Klein ; Donald Rossin
期刊名称：Informing Science: The International Journal of an Emerging Transdiscipline
印刷版ISSN：1547-9684
电子版ISSN：1521-4672
出版年度：1999
卷号：2
页码：33-43
出版社：Informing Science Institute
摘要：Although databases used in many organizations have been found to contain errors, little is known about the effect of these errors on predictions made by linear regression models. The paper uses a real-world example, the prediction of the net asset values of mutual funds, to investigate the effect of data quality on linear regression models. The results of two experiments are reported. The first experiment shows that the error rate and magnitude of error in data used in model prediction negatively affect the predictive accuracy of linear regression models. The second experiment shows that the error rate and the magnitude of error in data used to build the model positively affect the predictive accuracy of linear regression models. All findings are statistically significant. The findings have managerial implications for users and builders of linear regression models
关键词：Data Quality; E;rrors; Linear Regression