Business Analytics SEM 2 April 2026

Description

Business Analytics

Apr 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

Online food delivery platforms operate in highly dynamic environments where decisions must be driven by fast, reliable, and comprehensive data insights. These platforms generate massive volumes of structured transaction records, semi-structured system logs, and unstructured customer feedback in the form of reviews and social media posts. When these data types are analyzed in isolation, important patterns remain hidden and business understanding becomes fragmented. To overcome this limitation, organizations must apply data classification frameworks and integration strategies that allow diverse data sources to work together. By creating a unified analytics environment, management can gain a deeper view of customer behavior, operational efficiency, and market trends. This integrated approach supports better

 

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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

In data-driven decision making, hypothesis testing plays a crucial role in evaluating whether observed differences in customer behavior or satisfaction are statistically meaningful or simply due to random variation. For Mehta E-commerce, the use of one-sample and two-sample hypothesis tests to analyze survey responses across age-based customer segments represents a systematic attempt to move beyond intuition and base strategic decisions on evidence. However, the challenge arises when statistical results such as high p-values and overlapping confidence intervals create uncertainty about the strength of observed differences. Interpreting these results correctly is essential, as poor interpretation can lead to uniform strategies that

 

 

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

Understanding the relationship between student absenteeism and academic performance is essential for designing effective educational interventions. While simple linear regression can reveal general trends, real-world learning outcomes are influenced by multiple social and personal factors. For EduGrowth Schools, the challenge is not only to measure the impact of attendance on achievement but also to develop a predictive framework that supports targeted academic support. A well-structured regression-based approach can transform historical data into actionable

 

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

Forecasting production costs in a dynamic manufacturing environment requires models that can adapt to volatility and technological change. Traditional multiple regression models often struggle to remain accurate when operational conditions shift rapidly. For the manufacturing conglomerate, developing a future-ready cost forecasting framework is essential for maintaining profitability and operational efficiency. A modern regression-based strategy must integrate diverse data sources and support proactive planning.

Concept and Application

Integration of External and Operational Data

The new modelling strategy should incorporate both internal operational data and external economic indicators. Raw material market indices, energy price trends, and inflation metrics can be combined with