Business Analytics Dec 2026 Solved Assignments

Subject: Business Analytics

Sem 2 Dec 2026 Solved Assignments

28 Oct is the last date for assignment submission

Q1 A regional retail chain has recently integrated several years’ worth of sales data from multiple stores into a central Excel dataset. During initial analysis, analysts discovered many missing values in the ‘delivery amount’ column due to inconsistent point-of-sale entry practices across store locations. Senior management is concerned about the impact of missing data on profitability reporting and trend analysis, especially as the company prepares for a major expansion into new markets. The analytics lead must recommend an approach for handling missing values that balances data integrity and analytical reliability while maintaining comparability across different store datasets.How should the analytics lead apply appropriate imputation techniques (mean, median, or mode) to address missing values in the ‘delivery amount’ column? Discuss the implications of each method on analytical outcomes and propose a step-by-step Excel-based workflow that ensures the integrity and usability of the final dataset for profitability analysis across all store locations.

(10 Marks)

Q2 (A) A major airline is seeking to improve its on-time performance after receiving negative feedback from both customers and industry regulators. The airline’s analytics team has developed a probability-based machine learning model that predicts flight delays using historical flight data, weather patterns, aircraft types, and operational variables. However, industry experts argue that the model’s accuracy is limited by incomplete data and the assumption that all input variables are independent, which may not hold in a dynamic, multi-airport environment. The airline is considering whether to invest further in enhancing data quality or to revise its modelling approach to better reflect interdependencies.Evaluate the effectiveness of relying solely on probability-based models for predicting flight delays in the presence of incomplete data and variable interdependencies. What are the key risks and benefits for the airline, and how would you justify improvements to the modelling approach to maximise operational impact?

(5 Marks)

Q2 (B) FinStat, a mid-sized financial advisory firm, uses SLR to predict loan default rates from customer income. In a quarterly report, the analytics manager notes a low RSS

compared to TSS, resulting in a high R-squared near 0.9. However, the regression diagnostic review reveals heavy-tailed residuals not following the normal distribution, and some evidence of autocorrelation, especially during economic downturns. Leadership is considering using this model for major risk management decisions but is concerned about validity under stressed conditions.Critically evaluate the reliability of FinStat’s regression model for strategic loan portfolio management, given the high R-squared but violations in normality and autocorrelation assumptions. Justify your conclusions by weighing the trade-offs between statistical goodness-of-fit and model assumption breaches, recommending improvements for robust risk assessment.

(5 Marks)

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