EXAM QUESTIONS
1
FINAL EXAM
Task 2.3: Final Exam
Assessment type: Exam
Close date: 9 May. 25 (14:00) to 19 May. 25
(14:00)
Weight: 50%
Description: Student is required to answer all questions in the exam consisting of
the content
covered in Units 6 to 10
INSTRUCTIONS
FORMAT
BCOBM122 Applied Data Analysis (5 ETS)
Academic year 2024/25
Semester/Term Spring
Professor
Louisa Carlse, Pete Nicolau,
Resmi Allirani Gopalapillai
Participation in all assessment activities stated in this document is
required. The minimum pass grade for the course is 70. Due dates and times
are always in Geneva time. Unless otherwise stated, the submission point for
Turnitin activities will open exactly 10 days before the deadline. In the case of
exams, the questions will not be available to students in advance.
The Turnitin submission point for the Turnitin (Exam )will open exactly 10 days before the deadline.
The questions will not be available to students in advance.
• Student is required to answer all questions related to content covered in Unit 6, 7, 8, 9 and 10.
• All supporting calculations, workings, formulas and graphs done in MS Excel are to be included as
part of the Excel Workbook submission. Failure to submit supporting calculations or formulas will
result in reduced grades.
• Student is required to make use of supporting data provided to answer the questions. All questions
and all parts to the question is to be answered in full.
• Professional and clear formatting (including appropriate numbering) of questions in the Excel
Workbook is required. Please refer to the below formatting instructions required.
• Student is required to integrate and interpret course content to answer all questions. Please refer to
the detailed rubric for information on how each submission will be graded.
Your submission must meet the following formatting requirements:
• Number of files for submission: 1
EXAM QUESTIONS
2
• Required file format for main submission: Excel spreadsheet (.xlsx) Other details:
• Font: Arial
• Font size: 12
• Spacing: n/a
• Number of words: n/a
• Cover Page: Yes
• Bibliography: Yes.
• All refencing and citations require Harvard referencing style. Students must avoid plagiarism and
use the Harvard Referencing Guide and Turnitin to ensure that their sources are correctly cited.
Plagiarism includes the use of artificial intelligence tools, such as ChatGPT and Grammarly, when
output is copied and pasted from these sites. Please refer to the Academic Policies and
Procedures Manual and the Student Good Practice Manual in AI Literacy available on the
Student Services page for further details.
QUESTION 1: HISTOGRAM CREATION AND ANALYSIS IN EXCEL
You are given a dataset of monthly sales revenues for a retail business over the past two years.
1. Using Excel, create a histogram to visualize the distribution of sales revenues. Ensure to use the
“Data Analysis” toolpack.
2. Interpret the histogram, commenting on the skewness of the data and what this could imply for future
sales forecasting.
3. Based on your analysis, explain whether the business is experiencing consistent sales performance
or if there are fluctuations that may need attention.
QUESTION 2: LINEAR REGRESSION ANALYSIS IN EXCEL
You have data on housing prices, including features such as square footage, number of bedrooms, and age of
the house.
1. Using Excel, create a linear regression model to predict housing prices based on square footage.
2. Analyze the regression output: Interpret the R-squared value, the significance of the coefficients, and
the overall model fit.
3. Discuss the limitations of your model, such as assumptions about linearity, and suggest ways to
improve it.
QUESTION 3: HISTOGRAM COMPARISON
You are tasked with comparing the risk profiles of two investment portfolios over the past year.
1. Using Excel, create histograms for the returns of each portfolio.
2. Compare the histograms in terms of their skewness, spread, and shape. Discuss how these
differences may affect a potential investor’s decision.
3. Based on your interpretation, suggest which portfolio would be better suited for a risk-averse investor.
QUESTION 4: DATA MINING IN RAPIDMINER
EXAM QUESTIONS
3
You are provided with a dataset containing customer demographics and purchasing behavior.
1. Using RapidMiner, build a basic decision tree model to classify customers as high or low spenders
based on their demographic features.
2. Interpret the results of the decision tree, including the key variables influencing the classification.
3. Discuss how businesses can use this model to tailor marketing strategies or product offerings to
specific customer groups.
QUESTION 5: DATA-DRIVEN STORYTELLING
You are preparing a presentation for senior management on the impact of recent marketing campaigns.
1. Create a series of charts and visualizations in Excel to summarize the results of these campaigns
(e.g., sales growth, customer acquisition).
2. Based on your visualizations, write a brief narrative to effectively communicate the business impact of
the marketing campaigns. Be sure to highlight key insights that will guide future marketing decisions.
3. Discuss how the choice of visuals (e.g., bar charts vs. line graphs) influences the clarity and
effectiveness of the message.
