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The expectation-maximization algorithm is an approach in which values of the statistics which would be computed if a complete dataset were available are estimated (imputed), taking into account the pattern of missing data. In this approach, values for individual missing data-items are not usually imputed.
Set-Membership constraints: The values for a column come from a set of discrete values or codes. For example, a person's sex may be Female, Male or Non-Binary. Foreign-key constraints: This is the more general case of set membership. The set of values in a column is defined in a column of another table that contains unique values.
That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information.
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
Listwise deletion will exclude these respondents from analysis. This may create a bias as participants who do divulge this information may have different characteristics than participants who do not. Multiple imputation is an alternate technique for dealing with missing data that attempts to eliminate this bias.
The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...
The values can be considered erroneous and require further analysis for checking and determining the validity of the response. See the example below. In the above table is an example of extreme values in a data set also known as outliers. See Employees 2 and 6: The data is divergent from the rest of the table.
If actual outliers are not removed from the data set, they corrupt the results to a small or large degree depending on circumstances. If valid data is identified as an outlier and is mistakenly removed, that also corrupts results. Fraud: Individuals may deliberately skew data to influence the results toward a desired conclusion.