NMIMS Solved Assignments Business Analytics Dec 2026

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

Assignment Marks: 20
Instructions
NMIMS Centre for Distance and Online Education (NCDOE)
Course: Business Analytics
Internal Assignment Applicable for Dec 2026 Examination

Q1 A regional retail chain has recently integrated several years’ worth of sales data from
multiple stores into a central Excel dataset. During initial analysis, analysts
discovered many missing values in the ‘delivery amount’ column due to inconsistent
point-of-sale entry practices across store locations. Senior management is concerned
about the impact of missing data on profitability reporting and trend analysis,
especially as the company prepares for a major expansion into new markets. The
analytics lead must recommend an approach for handling missing values that balances
data integrity and analytical reliability while maintaining comparability across
different store datasets.How should the analytics lead apply appropriate imputation
techniques (mean, median, or mode) to address missing values in the ‘delivery
amount’ column? Discuss the implications of each method on analytical outcomes
and propose a step-by-step Excel-based workflow that ensures the integrity and
usability of the final dataset for profitability analysis across all store locations.
(10 Marks)
Q2 (A) A major airline is seeking to improve its on-time performance after receiving negative
feedback from both customers and industry regulators. The airline’s analytics team
has developed a probability-based machine learning model that predicts flight delays
using historical flight data, weather patterns, aircraft types, and operational variables.
However, industry experts argue that the model’s accuracy is limited by incomplete
data and the assumption that all input variables are independent, which may not hold
in a dynamic, multi-airport environment. The airline is considering whether to invest
further in enhancing data quality or to revise its modelling approach to better reflect
interdependencies.Evaluate the effectiveness of relying solely on probability-based
models for predicting flight delays in the presence of incomplete data and variable
interdependencies. What are the key risks and benefits for the airline, and how would
you justify improvements to the modelling approach to maximise operational impact?
(5 Marks)
Q2 (B) FinStat, a mid-sized financial advisory firm, uses SLR to predict loan default rates
from customer income. In a quarterly report, the analytics manager notes a low RSS
compared to TSS, resulting in a high R-squared near 0.9. However, the regression
diagnostic review reveals heavy-tailed residuals not following the normal distribution,
and some evidence of autocorrelation, especially during economic downturns.
Leadership is considering using this model for major risk management decisions but
is concerned about validity under stressed conditions.Critically evaluate the reliability
of FinStat’s regression model for strategic loan portfolio management, given the high
R-squared but violations in normality and autocorrelation assumptions. Justify your
conclusions by weighing the trade-offs between statistical goodness-of-fit and model
assumption breaches, recommending improvements for robust risk assessment.
(5 Marks)