Business Analytics
Jun 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? (10 Marks)
Ans 1.
Introduction
FreshStyles which is a nationwide retail chain, currently faces falling sales and increasing complaints about product availability. The root of the problem appears to have to do with poor data quality, resulting from multiple branches, where data sets contain missing values as well as duplicate data and different formats for product codes and dates. These inconsistencies render the analysis inconclusive, even using applications such as Excel which has led to low the confidence of stakeholders. As the lead data analyst your primary goal is to implement a structured data cleansing method to turn this data that is scattered into a reliable and consistent dataset. Once you have ensured the accuracy of your data, are meaningful insights able to be generated to improve inventory planning, forecasting demand, and general sales performance.
Concept and Application
Real-world analytics ensure that data quality is the main factor in the decision making process.
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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. (5 Marks)
Ans 2a.
Introduction
A company in the manufacturing industry is employing probability distributions for analyzing the quality of its products and identify defects. Making the right choice between Poisson as well as Exponential distributions is essential because it has a direct impact on how the defects are perceived, forecasted and managed. The choice should be in line with what the nature of data is and operating realities.
Concept and Application
Practically, choosing the proper distribution isn’t only an option for statistical reasons, but rather
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? (5 Marks)
Ans 2b.
Introduction
The regression model of the company provides a robust R-squared figure of 0.82 which indicates a positive connection between the amount of advertising spent and sales. But in reality, factors like festivals or disruptions produce variations that the model cannot explain. This is why it’s not advisable to rely entirely on models for deliberation.
Concept and Application
In terms of analytics and data analysis, a very high R-squared does not ensure total accuracy.