There are several methods commonly used to substitute inconsistent values in a data set. Here are a few examples:
Mean/Median Imputation: In this method, the missing or inconsistent values are replaced with the mean or median value of the corresponding variable. This method assumes that the missing values are missing completely at random and replaces them with the central tendency of the variable.
Regression Imputation: In this method, the inconsistent values are replaced by predicting their values based on a regression model. The model is built using the other variables in the data set that are not missing or inconsistent.
Hot Deck Imputation: In this method, the inconsistent values are replaced with values from similar cases in the data set. Similar cases are identified based on a set of matching variables. This method preserves the pattern of the data and replaces missing values with observed values from similar cases.
Multiple Imputation: This method involves creating multiple imputations for the inconsistent values based on statistical models. Multiple imputations allow for uncertainty in the imputed values and provide a range of plausible values for each inconsistent value.
Expert Knowledge: In some cases, domain experts or researchers may have specific knowledge about the data and can manually substitute inconsistent values based on their expertise. This method relies on human judgment and expertise.
It's important to note that the choice of method for substituting inconsistent values depends on the nature of the data, the extent of inconsistency, and the assumptions made about the missing or inconsistent values. Different methods may be more appropriate in different situations, and it's essential to consider the limitations and potential biases introduced by the chosen method.
Among the options provided, the correct method to substitute inconsistent values in a data set is "Editing." Editing involves identifying and correcting inconsistent or erroneous values in the data set. It typically involves a manual or automated review of the data to detect outliers, errors, or inconsistencies. Once identified, the inconsistent values can be corrected or replaced using appropriate methods such as imputation or removal. Coding and elimination, on the other hand, are not specific methods for substituting inconsistent values but can be part of the data cleaning process. Coding refers to assigning numerical codes or categories to represent certain values or variables, while elimination involves removing observations or variables from the data set.
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