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    Question

    Which of the following is the most appropriate way to

    handle missing values when performing data analysis in Excel using a Pivot Table?
    A Replace all missing values with zeros. Correct Answer Incorrect Answer
    B Ignore all missing values and proceed with the Pivot Table analysis. Correct Answer Incorrect Answer
    C Use the "Show items with no data" option to include missing values in the analysis. Correct Answer Incorrect Answer
    D Delete rows with missing values from the dataset. Correct Answer Incorrect Answer
    E Use a calculated field to replace missing values with the average of other values. Correct Answer Incorrect Answer

    Solution

    In Excel, when creating a Pivot Table, the "Show items with no data" option can be used to retain missing values without affecting the integrity of the analysis. This option allows the Pivot Table to display items that may not have data available for all records, effectively showing gaps where data is missing, but not excluding those rows entirely from the analysis. This is crucial when you're working with categorical data where some categories might not have any entries for certain periods or conditions but you want to include them in your analysis. Why Other Options Are Incorrect: • A: Replacing missing values with zeros is often misleading because it artificially inflates or distorts analysis. Zero might not be the most representative value for missing data, especially if it is categorical or non-zero-based. • B: Ignoring missing values may result in biased or incomplete analyses, especially in datasets where missing values are not random but follow a pattern that could affect the outcomes. • D: Deleting rows with missing values might lead to data loss and reduce the dataset size, which could affect the statistical power of the analysis, especially when the missing data is systematic. • E: Using a calculated field to replace missing values with averages could also lead to misrepresentations, particularly when missing values are not random. The average might not represent the true distribution of the data and could distort the analysis.

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