Business Analytics DEC 2025
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)
Ans 1.
Introduction
Analytics dashboards are particularly important tools for managers to keep an eye on sales, delivery patterns, and the general profitability of the firm in today’s retail world. However, the reliability of these dashboards depends on the quality and completeness of the underlying dataset. Missing values—particularly in key metrics like delivery amount—can distort insights, weaken trend analysis, and lead to flawed decision-making. Therefore, business analysts must adopt suitable imputation techniques to handle missing data, ensuring accuracy and consistency. The choice between mean, median, or mode imputation is not arbitrary; it requires a careful assessment of the dataset’s distribution, presence of outliers, and the business context in which decisions will be applied. Addressing these
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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)
Ans 2a.
Introduction
Customer satisfaction is a vital driver of competitive advantage for e-commerce companies, directly influencing retention, repeat purchases, and brand reputation. Mehta E-Commerce, after applying statistical inference, has identified product quality, delivery speed, and customer support as significant factors shaping customer experience. However, the challenge lies in allocating limited resources effectively across these dimensions. The management must carefully balance statistical significance with practical business
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)
Ans 2b.
Introduction
In business analytics, regression models are widely used to forecast key outcomes, such as sales, based on explanatory variables like advertising spend. A high R-squared value often creates the perception that the model is highly reliable. However, R-squared only explains the proportion of variance accounted for by the model and does not guarantee accuracy, causality, or predictive robustness. In the case of the retail company, management must
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