MIS771 Descriptive Analytics and Visualisations

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MIS771 Descriptive Analytics and Visualisation
DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS
DEAKIN BUSINESS SCHOOL
FACULTY OF BUSINESS AND LAW, DEAKIN UNIVERSITY
Assignment Two
Background
This is an individual assignment. You need to analyse the given dataset and then interpret and draw conclusions from your analysis. You then need to convey your findings in a written report to an expert in Business Analytics.
Percentage of the final grade
35%
The Due Date and Time
8 pm Thursday 20th May 2021
Submission instructions
The assignment must be submitted by the due date, electronically in CloudDeakin. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Please note that we will NOT accept any paper or email copies or part of the assignment submitted after the due date.
Information for students seeking an extension BEFORE the due date
If you wish to seek an extension for this assignment before the due date, you need to apply directly to the Unit Chair by completing the Assignment and Online Test Extension Application Form before Thursday 5 pm 20th May 2021. Please make sure you attach all supporting documentation and a draft of your assignment. The request for an extension needs to occur as soon as you become aware that you will have difficulty meeting the due date.
Please note: Unit Chairs can only grant extensions up to two weeks beyond the original due date. If you require more than two weeks or have already been provided with an extension by the Unit Chair and require additional time, you must apply for Special Consideration via StudentConnect within three business days of the due date.
Conditions under which an extension will usually be considered include:
• Medical – to cover medical conditions of a severe nature, e.g. hospitalisation, severe injury or chronic illness.
Note: temporary minor ailments such as headaches, colds, and minor gastric upsets are not severe medical conditions and are unlikely to be accepted. However, severe cases of these may be considered.
• Compassionate – e.g. death of a close family member, significant family and relationship problems.
• Hardship/Trauma – e.g. sudden loss or gain of employment, severe disruption to domestic arrangements, a victim of crime.
Note: misreading the due date, assignment anxiety, or multiple assignments will not be accepted as grounds for consideration.
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Information for students seeking an extension AFTER the due date
If the due date has passed, you require more than two weeks extension, or you have already been provided with an extension and require additional time, you must apply for Special Consideration via StudentConnect. Please be aware that applications are governed by University procedures and must be submitted within three business days of the due date or extension due date.
Please be aware that in most instances, the maximum amount of time that can be granted for an assignment extension is three weeks after the due date, as Unit Chairs are required to have all assignment submitted before results/feedback can be released back to students.
Penalties for late submission
The following marking penalties will apply if you submit an assessment task after the due date without an approved extension:
• 5% will be deducted from available marks for each day, or part thereof, up to five days.
• Work submitted more than five days after the due date will not be marked; you will receive 0% for the task.
Note: ‘Day’ means calendar day.
The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date.
Additional information: For advice regarding academic misconduct, special consideration, extensions, and assessment feedback, please refer to the document “Rights and responsibilities as a student” in the “Unit Guide and Information” folder under the “Resources” section in the MIS771 CloudDeakin site.
The assignment uses the dataset file A2T12021.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-2.
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Assurance of Learning
This assignment assesses the following Graduate Learning Outcomes and related Unit Learning Outcomes:
Graduate Learning Outcome (GLO)
Unit Learning Outcome (ULO)
GLO1: Discipline-specific knowledge and capabilities – appropriate to the level of study related to a discipline or profession.
GLO2: Communication – using oral, written and interpersonal communication to inform, motivate and effect change
GLO5: Problem Solving – creating solutions to authentic (real world and ill-defined) problems.
GLO6: Self-Management – working and learning independently, and taking responsibility for personal actions
ULO 1: Apply quantitative reasoning skills to solve complex problems.
ULO 2: Plan, monitor, and evaluate own learning as a data analyst.
ULO 3: Deduce clear and unambiguous solutions in a form that they useful for decision making and research purposes and for communication to the wider public.
Feedback before submission
You can seek assistance from the teaching staff to ascertain whether the assignment conforms to submission guidelines.
Feedback after submission
An overall mark, together with feedback, will be released via CloudDeakin, usually within 15 working days. You are expected to refer and compare your answers to the feedback to understand any areas of improvement.
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The Case Study
RogerLake is a leading Australian supermarket chain with 500 stores. Originating from a family-based general store, RogerLake now has stores all over Australia, with the first one being established in 1974. Individual store managers of RogerLake have wide-ranging powers about the day-to-day operations of their stores. However, RogerLake’s strategic planning and direction take place in the company Head Office in Adelaide.
RogerLake is anticipating a shift in the business climate within the next five years. The Head Office team is keen to implement the changes introduced during COVID-19 across the supermarket chain. They are confused about the store manager’s lack of enthusiasm to open their stores 24×7 or launch an accompanying eStore, given that the Head office has invested heavily in a digital platform, self-checkout machines and staff.
Subsequently, the Head Office management team has approached ANALYTICS7 and asked them to conduct a study to understand the characteristics of RogerLake stores and their business performance.
The Data
For this study, ANALYTICS7 has collected two sets of Data:
1. The first dataset is a random sample of 150 stores extracted from the company’s data mart. A complete listing of variables, definitions, and an explanation of their coding are provided in Working Sheet “Variable Description.”
2. The second dataset is about quarterly sales of RogerLake stores. The details of the Time-Series data is available on Working Sheet “Quarterly Sales.”
Your Role in ANALYTICS7
You are a modeller at ANALYTICS7. The team leader (Hugo Barra – MBA and MSc in DataScience) has asked you to lead the modelling component for the RogerLake project. Your need to review and complete the modelling activities as per the document. The minutes of the team meeting is below.
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Form 210-3
ANALYTICS7 Team Meeting
ANALYTICS7
727 Collins St, Docklands VIC 3008
Phone: (+61 3 212 66 000)
infor@analytics7.com.au
Reference
AP-210 RogerLake Project
Revised
24th April 2021
Level
Expert Analysis
Meeting Chair
Hugo Barra
Date
24 April 2021
Time
11:00 AM
Location
ANALYTICS7 L4.340
Topic
RogerLake Research Project – Analytics Details
Meeting Purpose:
Specifying and Allocating Data Analytics Tasks
Discussion items:
• Modelling Store Sales.
• Modelling the likelihood of a store opening 24×7
• Modelling the likelihood of a store launching an accompanying eStore
• Forecasting Quarterly Sales for the upcoming four quarters.
• Producing a technical report.
Detailed Action Items
Who:
Modeller
What:
1. Build a regression model to estimate Store Sales.
2. Hugo has performed a separate regression analysis and found that the number of competitors is a significant predictor of Store Sales. He believes that the relationship between Store Sales and the number of competitors should be weaker for those stores that are open 24×7. Model the interaction between the variables to test Hugo’s assumption and comment whether there is sufficient evidence to conclude that the interaction term is statistically significant in the model.
3. Build a model to predict the likelihood of a store opening 24×7.
4. Finalise Hugo’s model to predict the likelihood of a store launching an eStore.
4.1. Hugo has completed the initial analysis for this task. He has narrowed down the key predictors of the likelihood of a store launching an eStore to “Manager’s Age, Experience and Gender”. Your task is to continue his work and develop a model to ascertain the “likelihood of a store launching an eStore”.
4.2. Hugo is specifically interested in understanding the probability of stores that meet the following criteria to launch an eStore:
Those stores with managers,
a) in their mid-thirties;
b) with varying levels of managerial experience (i.e. 2-16 years?);
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c) and across both male and female store managers.
He believes that the store manager’s age, managerial experience, and gender may influence the decision to launch an eStore. RogerLake wishes to know whether to recruit tech-savvy young store managers for their stores. Accordingly, your job is to visualise the predicted probability of launching an eStore with the attributes described earlier.
5. Develop a time-series model to forecast RogerLake’s Sales for the next four quarters.
6. Write a report detailing all aspects of the analysis above (items 1-5).
The report should be as detailed as possible and should describe all critical outputs of the analysis. The results of the analysis should drive the recommendations to RogerLake management.
7. The ability to submit work on time is a highly sought-after skill at ANALYTICS7. As a part of your ongoing professional development, I would like you to report how you plan to deliver the outputs on or before the set date.
Next meeting
Thursday 20th May 2021
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Appendix: Explanatory Notes
To accomplish allocated tasks, you need to examine and analyse the dataset (A2T12021.xlsx) thoroughly. Below are some guidelines to follow:
Task 1. – Model building
You should follow an appropriate model building process. You should include all steps of the model building activities (especially all relevant pre and post model diagnostics) in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 1.2.a., 1.2.b., 1.2.c. etc.). If you make any reasonable/realistic assumption about the parameters, please note them next to the analysis.
Your technical report should clearly explain why the model might have undergone several iterations. Also, you must provide a detailed interpretation of ALL elements of the final model/regression output.
Task 2. – Interaction effect
To accomplish this task, you need to develop a new regression model using ONLY the factors discussed in the team meeting (Item 2). If you make any reasonable/realistic assumptions about the parameters, please note them next to the analysis.
Your technical report should clearly explain the role of each variable included in the model and use visualisation to illustrate the interaction effect. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.
Task 3. – Model building
You should follow an appropriate model building process. You should include all steps of the model building activities (especially all relevant pre and post model diagnostics) in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 3.1, 3.2). If you make any reasonable/realistic assumptions about the parameters, please note them next to the analysis.
You are required to discuss all details of your predictive model/logistics regression output.
Task 4.1 – Model building
You should follow an appropriate model building process. You should include all steps of the model building activities (especially all relevant pre and post model diagnostics) in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 4.1, 4.1.a). If you make any reasonable/realistic assumptions about the parameters, please note them next to the analysis.
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You are required to discuss all details of your predictive model/logistics regression output.
Task 4.2. – Visualising and interpreting predicted probabilities
Your technical report must include the predicted probability visualisation and the practical recommendations. These recommendations should answer the following question:
“How changes in store manager’s experience, age and the gender affect the predicted probabilities of launching an eStore.”
Task 5 – Forecasting Turnover
Past quarterly sales are in the Excel file. Your job is to develop a suitable model to forecast Quarterly Sales for the next four quarters.
In your technical report, you must explain the reason for selecting the forecasting method to forecast future quarterly sales. The report also must include a detailed interpretation of the final model (e.g. a practical interpretation of the time-series model…etc.)
Task 6. – Technical report
Your technical report must be as comprehensive as possible. ALL aspects of your analysis and final outputs must be described/interpreted in detail. Remember, your audience are experts in analytics and expect a very high standard of work from your report. High standards mean quality content (demonstrated attention to details) as well as an aesthetically appealing report.
Note: The use of technical terms is encouraged and expected in this assignment.
Your report should include an introduction as well as a conclusion. The introduction begins with the purpose(s) of the analysis and concludes by explaining the report’s structure (i.e., subsequent sections). The conclusion should highlight the essential findings and explain the main limitations. There is no requirement for a table of content or an executive summary.
Task 7 – Assignment planning and execution
The purpose of this practical task is to help you keep track of your progress with the assignment and submit it on time. To report how you plan your assignment and turn the plan into action, you must complete the tables provided in dot points as clearly as possible. Remember, effective planning, execution, and completing given tasks on time are essential professional development skills.
Note: Dot point writing requires you to use ‘point form’, not complete sentences.
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Submission Guide
The assignment consists of three documents: 1) Analysis and 2) Technical Report, and 3) Assignment Planning and execution tables.
1) Analysis
The analysis should be submitted in the appropriate worksheets in the Excel file. Each step in the model buildings should be included in a separate tab (e.g. 1.1.a., 1.1.b., …; and 2.2.a. 2.2.b., …). Add more worksheets if necessary.
Before submitting your analysis, make sure it is logically organised, and any incorrect or unnecessary output has been removed. Marks will be deducted for poor presentation or disorganised/incorrect results. Your worksheets should follow the order in which tasks are allocated in the minutes of the team meeting document.
Note: Give the Excel file the following name A2_YourStudentID.xlsx (use a short file name while you are doing the analysis).
2) Technical Report
Your technical report consists of four sections: Introduction, Main Body, Conclusion, and Appendices. The report should be approximately 2,500 words.
Use proper headings (i.e., 1., 2.1., 2.2., …) and titles in the main body of the report. Use sub-headings where necessary.
Visualisations / statistical output allowed in the report are:
1. Interaction effect plots
2. Predicted probability plots.
All other visualisations should ideally be in the Appendices (appendices are not included in the word count).
Make sure these outputs are visually appealing, have consistent formatting style and proper titles (title, axes titles etc.), and are numbered correctly. Where necessary, refer to these outputs in the main body of the report.
Note: Give the report the following name A2_YourStudentID.docx.
3) Assignment Planning and Execution Tables
The assignment planning and execution details should be submitted in the appropriate tables provided. The tables should be in dot points. Before filling in the tables, students are strongly encouraged to watch the pre-recorded workshop called ‘How to plan an assignment and turn the plan into action?’ by a Language and Learning Adviser.
Note: Give the assignment planning and execution file the following name A2_Planning_YourStudentID.docx


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