Business Analytics April 2026 Solved Assignments
Description
Business Analytics | Applicable for April 2026 Examination
Q1 An online food delivery marketplace collects vast arrays of structured (transaction times, payment amounts), semi-structured (order logs in JSON), and unstructured data (customer reviews and social media posts). The data science team faces difficulties integrating all these data types for insightful analytics, as trends in one type are often missed when isolated from others. Management now expects the team to use data type identification frameworks and integration strategies to unify the analysis and extract comprehensive business intelligence.Using the classification of data types discussed in the chapter, explain how you would apply these frameworks to integrate structured, unstructured, and semi-structured data for holistic analytics. What business benefits could arise from this integrated approach, and what challenges must you address in the preprocessing stage to enable unified insights? (10 Marks)
Ans 1.
Introduction
In the contemporary digital economy, online marketplaces produce substantial amounts of diverse data, encompassing structured transactional data, semi-structured order logs, and unstructured customer feedback or social media content. Although each data type offers valuable insights, analysing them independently frequently overlooks cross-sectional patterns that are essential for strategic decision-making. Implementing robust data type identification frameworks facilitates systematic classification and informs suitable integration strategies, thereby enabling data scientists to consolidate diverse
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Q2. After implementing targeted process improvements based on customer survey analysis, Mehta E-commerce noticed variable results across different customer segments. While younger customers responded favorably to faster delivery, older demographics prioritized product quality and support. The analytics team utilized one-sample and two-sample hypothesis tests to quantify differences among these groups but struggled to interpret high p-values and overlapping confidence intervals. Senior management must decide whether to pursue uniform changes or segment- specific strategies in response to these findings, weighing the risk of misallocating resources and alienating certain customer groups.Assess the effectiveness of Mehta E- commerce’s application of hypothesis testing to support segment-specific versus uniform intervention strategies. How should management interpret high p-values and overlapping confidence intervals in this context, and what further analytical or sampling approaches could help justify a targeted customer satisfaction strategy? (10 Marks)
Ans 2.
Introduction
Mehta E-commerce’s endeavour to enhance customer satisfaction underscores the pivotal function of statistical analysis in guiding strategic choices. The analytics team employed one-sample and two-sample hypothesis tests to discern disparities in customer segment responses, specifically concerning preferences for expedited delivery versus product quality. Nevertheless, elevated p-values and overlapping confidence intervals have engendered ambiguity concerning the statistical significance of the observed differences, thereby hindering the determination of whether to implement uniform process enhancements or segment-specific interventions. Consequently,
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Q3(A). At EduGrowth Schools, the management team is analyzing the intricate connection between student absenteeism and academic achievement using a simple linear regression approach. They have compiled a robust dataset spanning several years, including precise attendance logs and academic scores for each term. However, the team observes that frequent absenteeism sometimes coincides with low performance, but they suspect external influences such as family background and health could also play a significant role. With stakeholders demanding targeted interventions, EduGrowth must produce a predictive framework that not only reveals this relationship but also guides future instructional support.Design a comprehensive regression-based framework that synthesizes the available absenteeism and academic data, accounting for potential external influences. Propose innovative strategies for model construction, validation, and practical intervention planning to maximize student academic outcomes while mitigating the effects of absenteeism. Justify each aspect of your design. (5 Marks)
Ans 3a.
Introduction
At EduGrowth Schools, it is crucial to comprehend the influence of student absenteeism on academic performance to facilitate well-informed educational strategies. Although initial observations indicate a negative correlation between attendance and achievement, external factors, including family dynamics, health status, and socio-economic circumstances, may also exert an influence on outcomes. To aid in evidence-based decision-making, the administration necessitates a comprehensive predictive framework designed to quantify these effects and pinpoint actionable insights. The implementation of a regression-based model facilitates a systematic analysis of absenteeism in conjunction
Q3 (B). A manufacturing conglomerate seeks to forecast production costs using multiple regression. The initial analysis includes variables such as raw material prices, labour costs, production volume, and maintenance hours; however, high-frequency fluctuations in these factors alongside new technologies being introduced have rendered existing models less predictive. Leadership wants a future-ready model that can anticipate volatility and evolving operational patterns while providing actionable insight for production planning.Develop a novel regression-based modelling and validation strategy that integrates external data sources, predictive scenario analysis, and adaptive model updating to future-proof cost forecasting. Outline how your strategy would balance immediate interpretability with long-term adaptability and support proactive, data-driven manufacturing decisions. (5 Marks)
Ans 3b.
Introduction
Accurate cost forecasting is a cornerstone of modern manufacturing, essential for strategic planning, budgeting, and keeping operations running smoothly. However, traditional regression models, which lean heavily on past internal data, frequently miss the mark. They struggle to account for the volatility introduced by changing raw material costs, labour fluctuations, varying production volumes, and the ever-evolving technological landscape. For a manufacturing giant hoping to stay ahead, a forecasting system built for the future needs to pull in data from a variety of sources, both inside and outside the company. It must be able to adjust quickly to operational changes and offer
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