NMIMS Solved Assignment Business Analytics June 2026

NMIMS Solved Assignment Business Analytics June 2026 :  [Unique]
NMIMS Solved Assignment Business Analytics June 2026 :

Assignment Marks: 20
Instructions
PLEASE NOTE: This assignment is application based, you have to apply what you have
learnt in this subject into real life scenario. You will find most of the information through
internet search and the remaining from your common sense. None of the answers appear
directly in the textbook chapters but are based on the content in the chapter

NMIMS Centre for Distance and Online Education (NCDOE)
Course: Business Analytics
Internal Assignment Applicable for Jun 2026 Examination
l All Questions carry equal marks
l All Questions are compulsory
l All answers to be explained in not more than 1000 words for question Q1 and for question Q2(A)
and Q2(B) in not more than 500 words for each subsection. Use relevant examples, illustrations as
far as possible
l All answers to be written individually. Discussion and group work is not advisable.
l Students are free to refer to any books/reference material/website/internet for attempting their
assignments, but are not allowed to copy the matter as it is from the source of reference.
l Students should write the assignment in their own words. Copying of assignments from other
students is not allowed
l Students should follow the following parameter for answering the assignment questions

Q1 A national retail chain, Fresh Styles, 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 Fresh Styles, 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)
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)
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)