Web Reference: Missing data is an unfortunate reality of statistics. However, there are various ways to estimate and deal with missing data. This paper explores the pros and cons of traditional imputation methods vs maximum likelihood estimation as well as singular versus multiple imputation. To impute missing values for a classification variable, you can use a logistic regression method when the classification variable has a binary or ordinal response, or use a discriminant function method when the classification variable has a binary or nominal response.” Missing data is a common issue, and more often than not, we deal with the matter of missing data in an ad hoc fashion. The purpose of this seminar is to discuss commonly used techniques for handling missing data and common issues that could arise when these techniques are used.
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