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IIBA Certification in Business Data Analytics (IIBA-CBDA) · IIBA
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The analytics team would be concerned about variation because: Data Inconsistency: It can signal outliers, noise, or errors in how data was collected or processed across different cities . Result Validity: High variation can compromise the statistical significance and confidence of the results, making it harder to determine which day truly improves sales
A. Variation
OK! The answer is in fact A. See pg 123 in the CBDA guide on .2 Inferential Statistics. Yes, the excerpt from the CBDA guide aligns well with the answer. Here's how: Variation: INITIALLY, the analytics team would be concerned with variation to understand the differences in sales data across the cities. This is a fundamental step to ensure that any observed differences are understood in the context of the campaign. Heteroskedasticity: AFTER collecting and analyzing the data, examining for heteroskedasticity would be appropriate. Heteroskedasticity refers to the situation where the variance of residuals (or errors) is not constant across all levels of the independent variable. This could reveal if the variability in sales is consistent or if it varies significantly across different days or cities.