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Page 1 Kaplan Business School Assessment Outline
Assessment 2 Information
Subject Code: DATA4400
Subject Name: Data-driven Decision Making and Forecasting
Assessment Title: Evaluating forecasting-based analytics
Assessment Type: Individual Report
Word Count: 1000 Words (+/-10%)
Weighting: 30 %
Total Marks: 30
Submission: Turnitin
Due Date: Tuesday, Week 10 23.55pm AEST
Your Task
Given a dataset with multivariate time series data, you are to conduct multiple forecasting methods
and provide a description and interpretation of the techniques used.
The report is worth 30 marks (see rubric for the allocation of these marks).
Assessment Description
A dataset will be provided to you at the beginning of week 9. The objective of the assessment is
to build different forecasting models using Orange Data Mining and Tableau. Students must
calculate the Root Mean Square Error (RMSE) or Mean Absolute Percentage Error (MAPE) to
evaluate the performance and accuracy of the model, as well as choose the appropriate metrics
for model selection.
Page 2 Kaplan Business School Assessment Outline
Assessment Instructions

Report Structure and Content
Imagine you work for the Central Bank of Genovia and your task is to forecast the
unemployment rate in one quarter.
1. Import the DATA4400_A2_Data.csv dataset into Orange Data Mining
(https://orangedatamining.com/).
2. Assess the quality of the data in terms of missing values and provide summary statistics
of the variables.
3. Using an ARIMA model, forecast the unemployment rate for one quarter.
a. What is the forecast unemployment rate based on the ARIMA model
b. Provide a screenshot of the ARIMA model settings and the appropriate
visualisation for your forecast.
4. Using a VAR model, forecast the unemployment rate for one quarter.
a. What is the forecast unemployment rate based on the VAR model
b. Provide a screenshot of the VAR model settings and the appropriate visualisation
for your forecast.
5. How do the Fed Funds rate and the unemployment rate affect each other in Genovia
6. Use Tableau (https://www.tableau.com/academic/students) to visualise the dataset and
generate a forecast of the unemployment rate at the end of the next quarter.
7. What is the unemployment rate forecasted by Tableau
8. Explain which model was used in Tableau and report on its parameters.
9. Evaluate the models using the available metrics and report which model provides the
best forecast.
10.Summary
Page 3 Kaplan Business School Assessment Outline
Important Study Information
Academic Integrity Policy
KBS values academic integrity. All students must understand the meaning and consequences
of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct
Policy.
What is academic integrity and misconduct
What are the penalties for academic misconduct
What are the late penalties
How can I appeal my grade
Click here for answers to these questions:
http://www.kbs.edu.au/current-students/student-policies/.
Word Limits for Written Assessments
Submissions that exceed the word limit by more than 10% will cease to be marked from the
point at which that limit is exceeded.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the
resources on the MyKBS Academic Success Centre page. Click here for this information.
Page 4 Kaplan Business School Assessment Outline
Assessment Marking Guide
Standards for this Task
Points Feedback
Forecasting Results
Loaded data into Orange and identified relevant widgets.
Identified information that is relevant to building the required
models.
Able to identify information relevant to facilitate the understanding
of ARIMA and VAR models.
Developed models and created forecasts.
Adequately identified variables causing unemployment rate.
/15
Interpretation
Compared results with outputs from a BI tool and/or Exploratory
Included a figure of Orange workflow with explanation of the output.
Identified any inconsistencies in output.
Provided interpretations that are within the scope of the subject
and assessment.
/10
Report and Summary:
Structured such that the reader can grasp key points from the analysis.
Key headings are included.
Justification of assumptions and interpretations are clear and concise.
In-line referencing used and references are relevant and genuine.
Visualisations are used to convey key arguments.
/5
/30