Statistical data preparation: management of missing values and outliers

By S. Kwak, Jong Hae Kim. Korean Journal of Anesthesiology, 2017

612 citations recorded by the source corpus.

Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the results and degrades the efficiency of the data. Outliers significantly affect the process of estimating statistics (e.g., the average and standard deviation of a sample), resulting in overestimated or underestimated values. Therefore, the results of data analysis are considerably dependent on the ways in which the missing values and outliers are processed. In this regard, this review discusses the types of missing values, ways of identifying outliers, and dealing with the two.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c34f1b306d225bcf57bef83525da6e568f6381c1

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In Statistics and Probability

1. Dinesh Mendhe
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3. Kush Patel
4. Habiba Abdelhalim
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6. Saman Zeeshan

Citations in the corpus

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