Missing values in your data are always problematic, but how problematic they become depends heavily on the underlying missing data mechanism.The visualization below compares three common response mechanisms:馃敼 MCAR (Missing Completely at Random)馃敼 MAR (Missing at Random)馃敼 MNAR (Missing Not at Random)For each mechanism, the visualization gradually increases the proportion of missing values and compares how strongly the estimated mean of the target variable deviates from the true value.Two import
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Missing values in your data are always problematic, but how problematic they become depends heavily on the underlying missing data mechanism.The visualization below compares three common response mechanisms:馃敼 MCAR (Missing Completely at Random)馃敼 MAR (Missing at Random)馃敼 MNAR (Missing Not at Random)For each mechanism, the visualization gradually increases the proportion of missing values and compares how strongly the estimated mean of the target variable deviates from the true value.Two import
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