The second assignment aims to enhance your understanding of the roles and importance of business
analytics and its implementations in industry. You are also required to work on descriptive analytics in
practice. The assignment comprises three tasks. First task is to develop an extensive review report of
landscape of business analytics in industry. Second task is to develop a taxonomy for descriptive analytics
techniques, describing for each technique its purpose, functionality, assumptions, method of validation
and sample use case. The third task is to develop a linear regression model to predict the propensity score
of a specified event.
Task 01
Compile a review report that:
• describes the purpose, importance and role of business analytics in creating strategic value and
competitive advantage.
• defines analytics ecosystem (descriptive, predictive, prescriptive and exploratory analytics) and
illustrates how they are adopted by various industries in their key business functions ranging from
strategy, marketing and sales, operations (production), customer services etc.
• illustrates how the data mining process can be implemented and in particular, challenges in
implementing data mining and business analytics in agile business environments.
• describe challenges of achieving/cultivating analytic leadership and culture in practice.
Hint: please review all presentation slides and select the relevant knowledge points. You may also need
to perform research on literatures and industrial cases to explain and support your points.
Use academic, industrial and technical references and real case examples to support your views on each
of the above. The report is required to be written in a professional format conforming to report guidelines
noted below.
Task 02
Task 02 aims to assess your knowledge on descriptive analytics covered/mentioned in class. You are
required to develop a taxonomy for descriptive analytics techniques, describing for each technique its
purpose, functionality, assumptions, method of validation and sample use case. The sample use case must
be from a business analytics scenario. An example is shown below.
Task 03
Task 03 aims to assess your practical knowledge and skills to develop a linear regression model using R and
Excel. You are required to develop a linear regression model using the data provided. The data was
captured to evaluate the propensity scores of employees from a company to contract a flu during a winter
season. The data (‘Immunity.csv’) consists of three columns:
• Propensity: is the probability of an employee to catch the flu;
• Non_healthy_food: the average monthly expenses employees spent on none healthy food in dollar
value during past 2 months;
• Percent_inoffice: the percentage of time an employee works in the office during past 2 months.
Task 03 requirements:
• Explore and plot the correlation among variables (in Excel or R);
• Develop a linear regression model using R and Excel (you are required to submit both the excel file
and the R scripts)
• Illustrate and explain the key parameters of the model including coefficient of determination (R2),
Residues, fitted linear equation, etc.
• Identify the significant independent variable;
• Predict the propensity scores of three new employees contracting the flu from the following test
data (using 95% confidence interval):
Non_healthy_food = 290, Percent_inoffice = 6
Non_healthy_food = 500, Percent_inoffice = 64
Non_healthy_food = 300, Percent_inoffice = 80
Hint: in the last item of Task 03, propensity score prediction, Excel only has function for point prediction
of mean but R can be used for both point and interval prediction. To avoid complex calculations in Excel,
please use R for prediction with confidence intervals.