BUSINESS ANALYTICS JUNE 2026 Solved Assignment
Description
BUSINESS ANALYTICS
JUNE 2026 EXAMINATION
Q1 A national retail chain, FreshStyles, is facing declining sales and customer complaints about product availability. The management suspects that the underlying issue stems from inconsistencies in their sales and inventory data collected from multiple branches. Their current datasets contain missing values, duplicates, and inconsistent formatting in date and product codes. Despite using Excel for analysis, the results remain inconclusive and are met with skepticism by stakeholders. The company’s analytics team has been tasked with resolving these data issues to enable trustworthy business insights and inform better inventory and sales strategies. As the lead data analyst for FreshStyles, apply appropriate data cleansing techniques (including missing value treatment, duplicate removal, and format standardization) to this real- world dataset. Describe the sequential steps you would take and explain how your approach ensures data reliability and supports more effective business decision- making?
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Q2 (A) A manufacturing business has recently implemented a probability distribution analysis to better understand and reduce process defects. The operation team is considering whether to fit the data to a Poisson (discrete, PMF-based) or an Exponential (continuous, PDF-based) distribution. Corporate leadership is concerned about the accuracy and effectiveness of using each approach to drive quality improvement initiatives and continuous adaptation. Critically evaluate the merits and drawbacks of modeling defect data using Poisson versus Exponential distributions. Assess how the choice between the two would impact quality assurance, predictive accuracy, and the company’s adaptability to dynamic production environments, justifying your position.
Q2 (B) A consumer goods company deploys a simple linear regression model to predict monthly sales from advertising spend, yielding an R-squared value of 0.82. However, regional marketing managers note that in some months, major events (such as festivals and supply chain disruptions) may cause large, unpredictable deviations in sales that the regression model does not explain. The executive team must decide how much to trust the model outputs for future campaign planning, and whether to introduce more explanatory variables or develop alternative analytics approaches. Critique the company’s reliance on the current regression model for campaign planning in light of the marketing managers’ observations. How should the executive team weigh the strong R-squared value against external factors, and what improvements or complementary analyses would you recommend to enhance decision-making robustness?
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