Missing At Random
Missing at random (MAR) is a mechanism that describes the missingness of data in a dataset. In MAR, the probability of a data point being missing depends on the observed data in the dataset. This means that the missingness is not completely random, but rather depends on the values of other variables in the dataset. In other words, the missingness is not related to the missing value itself, but rather to other variables in the dataset. This mechanism is important in data analysis because it allows for the use of statistical methods to handle missing data. By understanding the mechanism of missingness, researchers can use statistical techniques such as multiple imputation or maximum likelihood estimation to handle missing data and obtain unbiased estimates of parameters.
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