Business Analytics NMIMS Assignment Solution June 2025

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Q1 Given a dataset with missing values, apply appropriate data treatment techniques to
handle the missing data. Justify your choice of method based on the nature of the
dataset. Additionally, analyze a real-world scenario where missing data impacts
decision-making, and implement suitable imputation methods to improve data quality
Student_I
D Name Age Gender
Math_Scor
e
English_Scor
e
Attendance
(%)
101 Aarav 20 F 85 88 95
102 Bhavya 21 M 78 88
103 Charan 22 M 82 92
104 Deepak M 92 91
105 Esha 20 F 88 85 97
106 Farhan 21 76 79 85
107 Gauri F 80 86 90
108 Harshita 22 F 90 93
109 Ishan 23 M 90 89
110 Jyoti 20 F 84 87
(10 Marks)
Q2 (A) A pharmaceutical company is testing a new drug for reducing blood pressure. They
conduct a clinical trial with two groups: one receiving the drug and the other
receiving a placebo. The blood pressure levels are recorded before and after the trial.
Analyse the components of a two-sample hypothesis test and determine why it
is appropriate or not for this study. (1 Mark)
1.
Given that the obtained p-value is 0.08, break down the decision-making
process for rejecting or failing to reject the null hypothesis at a 5% significance
level. (1 Mark)
2.
Examine the potential risks associated with Type I and Type II errors in this
study and discuss how they could affect the interpretation of results. (1 Mark)
3.
The company wants to check whether the drug’s effectiveness varies across
different age groups (e.g., 30-40, 41-50, 51-60). Analyse whether the Chi square test of independence is an appropriate test in this scenario. (1 Mark)
Differentiate between the Chi-square Goodness of Fit test and the Chi-square
test of independence, and analyse how each applies to different types of
pharmaceutical studies. (1 Mark)
5.
(5 Marks)
Q2 (B) A company wants to predict sales based on advertising expenses using a simple
linear regression model. The dataset for 5 months is given below:
Month Advertising Expense (X
in Rs 1000s)
Actual Sales (Y in
Rs 1000s)
Predicted Sales ( in
Rs 1000s)
1 2 4 3.8
2 3 5 5.2
3 5 7 6.9
4 7 10 9.5
5 9 12 11.7
1. Formulate the simple linear regression equation based on the given data.
Determine the regression coefficients (: Intercept, : Slope) and interpret their
impact on sales.
2.
Derive insights from the regression equation, understanding the baseline
performance and the impact of advertising expenses on sales.
3.
Suggest recommendations based on findings, highlighting the effectiveness
of advertising expenses.
4.
Instructions:
– Use Excel to compute the regression equation, coefficients, and R² value.
– Paste the Excel output with formulas to demonstrate calculations.
– Insights should be based on data from Excel analysis

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