# Posts Tagged ‘ missing data ’

## 3 problems with mean imputation

December 6, 2017
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In a previous article, I showed how to use SAS to perform mean imputation. However, there are three problems with using mean-imputed variables in statistical analyses: Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence

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## Mean imputation in SAS

December 4, 2017
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Imputing missing data is the act of replacing missing data by nonmissing values. Mean imputation replaces missing data in a numerical variable by the mean value of the nonmissing values. This article shows how to perform mean imputation in SAS. It also presents three statistical drawbacks of mean imputation. How

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## Visualize patterns of missing values

November 29, 2017
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Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example,

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## Create patterns of missing data

October 26, 2016
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When simulating data or testing algorithms, it is useful to be able to generate patterns of missing data. This article shows how to generate random and systematic patterns of missing values. In other words, this article shows how to replace nonmissing data with missing data. Generate a random pattern of

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## Visualize missing data in SAS

April 20, 2016
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You can visualize missing data. It sounds like an oxymoron, but it is true. How can you draw graphs of something that is missing? In a previous article, I showed how you can use PROC MI in SAS/STAT software to create a table that shows patterns of missing data in

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## Examine patterns of missing data in SAS

April 18, 2016
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Missing data can be informative. Sometimes missing values in one variable are related to missing values in another variable. Other times missing values in one variable are independent of missing values in other variables. As part of the exploratory phase of data analysis, you should investigate whether there are patterns

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## 3 Tips for Testing Large Data on Slow Connections or Escaping Upper Hell

May 27, 2013
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Do you ever find yourself wondering if you are paying for sins from a past life while you wait for SAS Enterprise Guide to show ...

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