Question
Which of the following is the most appropriate way to
handle missing values when performing data analysis in Excel using a Pivot Table?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.
A company wants to reduce its high customer churn rate. As a data analyst, which metric is most important to focus on during your initial analysis?
Which of the following is a key advantage of using box plots over histograms for visualizing data?
In the context of risk modeling for credit scoring, which of the following factors is least likely to be used in predicting a person’s creditworthines...
Which of the following methods is most commonly used during data wrangling to handle missing values in a dataset?
When integrating multiple datasets, which approach helps resolve inconsistencies and create uniformity across all data sources?
Which visualization library in R is most effective for creating interactive, web-based data visualizations?
When conducting data validation to ensure data accuracy and completeness, which of the following methods would best verify that all entries in a dataset...
Which Business Intelligence tool is renowned for its interactive dashboards and visualization capabilities, commonly used in corporate reporting and dat...
Which of the following correctly handles multiple exceptions in Java?
Which cloud computing service model provides users with complete control over hardware resources like servers, storage, and networks?