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PharmaSUG China 2017 Paper 16Outlier analysis in SASFocus on the programming approach Chao Jiang, MSD, Beijing, China Yan Liu, MSD, Beijing, China ABSTRACT Outlier, sometime because of noise and pollution
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How to fill out outlier analysis in sas

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How to fill out outlier analysis in sas

01
Gather the data that you want to analyze for outliers.
02
Use the PROC UNIVARIATE procedure in SAS to calculate statistics such as mean, standard deviation, minimum, maximum, and quartiles for your data.
03
Use the OUTLIER statement in PROC UNIVARIATE to specify which statistics you want to use to identify outliers.
04
Examine the output from PROC UNIVARIATE to identify potential outliers based on the specified statistics.
05
Further investigate the potential outliers by looking at the distribution of the data and considering any contextual information that may explain the outliers.
06
Decide how to handle the outliers, whether it be removing them from the analysis, transforming the data, or using robust statistical methods to account for them.

Who needs outlier analysis in sas?

01
Researchers in various fields such as finance, healthcare, and social sciences who want to detect unusual or anomalous data points.
02
Data analysts who want to ensure the accuracy and reliability of their statistical analyses by identifying and addressing outliers in their data.
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Outlier analysis in SAS involves the identification and examination of data points that deviate significantly from other observations in a dataset, to assess their impact on statistical analysis.
Entities or individuals who manage data sets that may contain outliers, especially in research or regulatory contexts, are typically required to conduct and report outlier analysis in SAS.
To fill out outlier analysis in SAS, you generally need to import your dataset, apply statistical techniques such as Z-score or IQR to identify outliers, and generate a report summarizing the findings.
The purpose of outlier analysis in SAS is to improve the accuracy of statistical inferences by identifying unusual data points that may skew results or indicate errors in data collection.
The report on outlier analysis in SAS should include identified outliers, statistical measures used for determination, impact assessment on overall analysis, and any corrective actions taken.
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