MIS771 - Descriptive Analytics and Visualisation - Case Study Statistics Assignment Help

Assignment Task

Background

This is an individual assignment. You need to analyse a 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.

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 Friday 5 pm 17th Thursday September 2020. Please make sure you attach all supporting documentation and a draft of your assignment. The request for extension needs to occur as soon as you become aware that you will have difficulty in meeting the due date.

Please note: Unit Chairs can only grant extensions of 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 3 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, serious injury or chronic illness.

Note: temporary minor ailments such as headaches, colds and minor gastric upsets are not serious medical conditions and are unlikely to be accepted. However, serious 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.

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

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 that is 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 T22020MIS771_A2Data.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-2.

Assurance of Learning

This assignment assesses the following Graduate Learning Outcomes and related Unit Learning Outcomes:

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.

The Case Study

ANALYTICs7, a leading data analysis consulting company, has extensive experience in analysing data for both local and global, small to medium companies. By solving their business problems, ANALYTICs 7 helps these businesses to plan ahead and thrive.

Your Role in ANALYTICS7

Dr Hugo Barra, the lead data scientist at ANALYTICs7 has engaged you to lead the modelling component for the TPM and AP projects and construct a report of your key findings and recommendations in response to the questions posed in the meeting minutes of the last team meeting on the next page.

Datasets (accessible via T22020MIS771_A2Data.xlsx file)
There are two datasets available for this assignment: TPM_Employee_Attrition and Monthly_EnergyCon_MW 

Employee Survey data (TPM_Employee_Attrition )– TassPaperMill (TPM), a subsidiary of Pinnon Paper Industries (PPI), is an Australian company with a long history of manufacturing paper rolls. To address numerous concerns raised in their recent employee survey TPM is currently reviewing how they calculate salary increments for their employees. TPM has hired ANALYTICs7 to extract a random sample of 1470 employee records from their HR database. Their ultimate goal is to adopt a more holistic rewarding system factoring the key relations between remuneration indicators and demographic characteristics, employment history and various other potential contributors to boost performance. In addition, human resource manager at TPM reported in her recent presentation to the company executive management team that the staff turnover rate at TPM is higher compared to their competitors. Thus, TMP wants to identify key contributing factors before they lose more talented, motivated and focused employees who contribute to the organisation's overall success.

Energy consumption data (Monthly_EnergyCon_MW) – Australian Power (AP) is one of the largest generators of electricity in Australia, servicing for more than three million households in Victoria. AP operates an electric transmission system that covers much of Victoria and serves over 30% of the electricity demand in Victoria. This dataset consists of monthly power consumption data in megawatts (MW) comes from AP’s data warehouse during 2010-2019. AP wishes to review their current resources allocation strategy to plan and prioritise the provision of resources based on rapidly growing energy demand in Victoria.

A complete listing of variables is provided in the T22020MIS771_A2Data.xlsx file. Note: All data, reports, people and scenarios in this assignment are either fictitious or have been modified from their original state. Any similarity to actual events is purely coincidental. It has been produced for the sole purpose of assessing performance of summative assessment task 2.

Task 1 – Summarising dependent variables

The purpose of this task is to analyse and explore the key features of these variables individually. At the very least, you should thoroughly investigate relevant summary measures/charts and graphs of these variables. Proper visualisations should be used to illustrate key features. Your technical report should describe ALL critical aspects of each variable.

Task 2. – Model building (PercentSalaryHike)

You should follow an appropriate model building process. All steps (including pre and post model diagnostics) of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 2.2.a., 2.2.b., 2.2.c. etc.). You must make, and document, reasonable/realistic/practical assumptions about the parameters you are working with in Task 2.

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 3. – Interaction effect

To accomplish this task, you need to develop a new regression model using ONLY the factors discussed in the team meeting (Item 3). In other words, this section of the analysis is separate from the regression model constructed in Task 2. You must make, and document, reasonable/realistic/practical assumptions about the parameters you are working within Task 3.

Your technical report should clearly explain the role of each variable included in the model. A suitable visualisation technique should be provided. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.

Task 4.1 – Model building (likelihood of an employee leaving the company)

You should follow an appropriate model building process. All steps (including pre and post model diagnostics) of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model. You must make, and document, reasonable/realistic/practical assumptions about the parameters you are working within Task 4.

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 be supplemented by practical recommendations. These recommendations should answer the following question:

Task 5 – Forecasting Energy Consumption

Past monthly energy consumptions are given in the Excel file. Your job is to develop a suitable forecasting model to predict future energy demand for the next 12 months.

In your technical report, you must explain the reason for selecting the forecasting method to predict future energy demand. 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 means quality content (demonstrated attention to details) as well as an aesthetically appealing report.

Note: The use of technical terms is acceptable in this assignment

Your report should include an introduction as well as a conclusion. The introduction begins by highlighting the main purpose(s) of analysis and concludes by explaining the structure of the report (i.e., subsequent sections). The conclusion should highlight the key 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 important skills of your professional development.

Note: Dot point writing requires you to use ‘point form’, that is, not full sentences.

Submission Guide

The assignment consists of three documents: 1) Analysis and 2) Technical Report 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. 2.2.a., 2.2.b., 3.2.a. 3.2.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 should be submitted in the appropriate tables provided. The tables have to be completed in dot points. Before filling in the tables, students are strongly encouraged to attend a workshop called ‘How to plan an assignment and turn the plan into action?’ that will be run by a Language and Learning Adviser in week 2.

Note: Give the assignment planning and execution file the following name A2_Planning_YourStudentID.docx 

 

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