Question

When data is missing in a systematic way, you should determine the impact of the missing data on the results and whether missing data can be excluded from what?

Answer 1

Extrapolate the data or impute the missing data by filling in the average of the values around the missing data.

Imputation is the process of replacing the missing data with estimated values. Rather than deleting any case that has any missing value, this approach preserves all cases by replacing the missing data with a probable value estimated by other available information.

Data science may be a way of extracting insights from large volumes of disparate data. Data science involves drawing patterns from seemingly random structured and unstructured forms of data. The missing data adds ambiguity to the info. it's represented as NA or NAN. If the dataset is tiny then every datum counts.

The missing data creates imbalance within the observations and might even result in invalid conclusions. Mean, median and mode are the foremost popular averaging techniques, which are wont to infer missing values.

learn more about missing data: brainly.com/question/20429571

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