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Business
Analytics
Dec
2025 Examination
Q1.
A retail chain is preparing to launch a new analytics dashboard to monitor
sales performance. While compiling the sales dataset, the analyst notices that
several entries in the 'delivery amount' column are missing due to data entry
errors and system glitches. The dataset will be used to generate visualisations
for management decision-making. The analyst must select and apply the most
suitable imputation method to fill in the missing values, ensuring that the
resulting analysis accurately reflects business performance and is not skewed
by the chosen technique. Given the scenario, how should the business analyst
apply appropriate imputation methods to handle missing delivery amounts in the
sales dataset, and what considerations should guide the choice between mean,
median, and mode imputation for this retail context? (10 Marks)
Q2(A).
After applying statistical inference, Mehta E-Commerce identified several
factors—such as product quality, delivery speed, and customer support—that
significantly impact customer satisfaction. The company must now decide how to
allocate resources to address these areas, considering limited budgets and
competing business objectives. Assess the strategic implications of resource
allocation decisions made by Mehta E-Commerce after identifying statistically
significant factors affecting customer satisfaction. How should management
weigh the statistical significance of these factors against business
priorities, operational constraints, and potential unintended consequences when
justifying investments in improvement initiatives? (5 Marks)
Q2(B).
A retail company has implemented a simple linear regression model to forecast
monthly sales based on advertising spend. The analytics team reports a high R-
squared value, leading management to believe the model is highly reliable.
However, some team members question whether R-squared alone provides a complete
picture of model performance, especially given the complexity of market dynamics
and the risk of overfitting. Assess the effectiveness of using the coefficient
of determination (R- squared) as the primary metric for evaluating the fit of a
simple linear regression model in a business context. What are the potential
pitfalls of over-relying on R- squared, and how would you recommend balancing
it with other diagnostic tools to ensure robust model assessment? (5 Marks)
Dear students, get fully
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Do send your query at :
or call us at :
08263069601
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assignments available with 100% surety and refund)
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