Descriptive Analytics

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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 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.
Percentage of the final grade 35%
The Due Date and Time 8 pm Thursday 17th September 2020
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.
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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 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:
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.
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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.
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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-211 TPM Project
Revised 27th August 2020
Level Expert Analysis
Meeting Chair Dr Hugo Barra
Date 24 August 2020 Time 10:00 AM Location ANALYTICS7 L4.320
Topic TPM and AP Research Projects – Analytics Details
Meeting
Purpose: Specifying and Allocating Data Analytics Tasks
Discussion
items:
1. Variable(s) description
2. Modelling PercentSalaryHike
3. Modelling the likelihood of an employee leaving the company
4. Forecasting monthly energy consumption in Megawatts
5. Producing a technical report
Detailed
Action
Items
Who:
Modeller
What:
1. Providing an overall summary of the following two variables:
1.1. Percentage increase in salary (PercentSalaryHike)
1.2. Attrition
2. Identify potential variables that may influence PercentSalaryHike:
2.1. Identify a list of possible variables that influence percentage increase in
salary. Which three independent variables have the more impactful linear
relationship with PercentSalaryHike? What form of relationship(s) exist
between the independent variable(s) and PercentSalaryHike? Are there any
potential multi-collinearity problems? If so, which variables are they?
2.2. Build a regression model to estimate percentage increase in salary.
2.3. Perform residual analysis. Based on your residual plots, does there appear to
be any problems with the regression model?
3. Hugo has performed some preliminary analysis and discovered that the
performance rating is a significant predictor of the Percentage increase in salary.
Prior research shows that the strength of the relationship between performance
rating and percentage increase in salary may vary according to satisfaction with
the job. Generally speaking increased job satisfaction creates a more productive
workforce as they are more motivated to improve their job performance.
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Therefore, Hugo believes that the relationship between performance rating and
percentage increase in salary should be stronger for employees who are satisfied
with their jobs.
3.1. Model the interaction between the variables to test Hugo’s assumption.
3.2. Comment on whether there is sufficient evidence to conclude that the
interaction term in the model is statistically significant.
4. A model to predict the likelihood of an employee leaving the TPM
4.1. Hugo has already performed an analysis with Attrition and Age, Environment
Satisfaction, Overtime and Years in current role as the independent
variables. Continue to refine his work and develop a model to ascertain the
likelihood of an employee leaving the TPM.
4.2. Hugo is specifically interested in understanding how the following aspects
drive employee attrition.
a) Medium satisfaction level with their working environment and job,
and 5 years since their last promotion
b) Number of years in current roles and whether they work overtime
c) 45 years old married employee with a very-high level job
classification and maintaining a good work-life balance.
In order to gain an edge in the current very competitive talent market, Hugo
believes attaining a very good understanding in what drives employees to
quit is well worth the time and investment. In addition, TPM should take
prompt actions to mitigate increasingly high employee turnover costs which
could be up to twice an employee’s salary depending on their position.
Accordingly, your job is to visualise the predicted likelihood of employee
attrition with the specific attributes described above.
5. Develop a time-series model to forecast AP’s energy consumption for the next 12
months. How are summer predictions different from those for winter?
6. Provide a written report detailing ALL aspects of your analysis. The report should
be as detailed as possible and should describe ALL key outputs of the analysis. The
results of the analysis should drive the recommendations to the
executives/decision makers at both TPM and AP.
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 agreed outputs on or before the set date.
Next
meeting
Friday 18th September 2020
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Appendix: Explanatory Notes
To accomplish allocated tasks, you need to examine and analyse the dataset
(T22020MIS771_A2Data.xlsx) thoroughly. Below are some guidelines to follow:
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:
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“How will changes in the number of years in the current rol, including whether they
work overtime, affect the likelihood of attrition for 45 years old, married employees
with a very-high level job classification who are now celebrating five years since their
last promotion and maintaining a good work-life balance with a medium degree
satisfaction level in their working environment and with their current job?”
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.
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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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Criteria Name Not Attempted Needs Improvement Satisfactory Good Very Good Exemplary
Analysis
(35%)
GLO1
GLO5
GLO6
5%
Does not use any
appropriate descriptive
analysis tool.
Use irrelevant or
inappropriate descriptive
analysis tool.
Use appropriate descriptive
analysis tool but there are errors
in the analysis.
Most relevant descriptive analysis
tools are used BUT there are
minor errors in the analysis.
All relevant descriptive analysis
tools are used with minor errors
in the analysis.
Skilful and comprehensive descriptive
analysis of all relevant variables using
variety of techniques.
10%
Does not use any
appropriate bivariate
exploratory data analysis
tool.
Use irrelevant or
inappropriate bivariate
analysis tool.
Use appropriate bivariate analysis
tool to identify IVs, but there are
errors in the analysis.
Appropriate bivariate analysis tool
is used, but not all relevant IVs are
identified.
All relevant IVs are identified
using proper bivariate analysis
technique, but minor issues
noted.
Skilful and comprehensive analysis of
bivariate relationships is presented and
all relevant IVs are identified.
Either inappropriate
predictive model is
developed and/or analysis
lacks All steps of modebuilding process missing.
Relevant IVs are included
in the predictive model,
BUT some steps of modelbuilding process missing.
A predictive model is developed
with All model-building steps
included, BUT the final model is
incorrect and/or there are many
errors in the analysis.
An appropriate predictive model
is developed with All modelbuilding steps presented BUT
there are minor errors in the
analysis.
The final model includes those IVs
that have predictive power with
All steps in model-building
process clearly presented.
Model-building process is presented in
logical/comprehensive manner AND the
final model is correct.
Interaction analysis is
missing.
Interaction analysis is
incorrect.
Analysis of interaction effects is
presented BUT there are many
errors.
Interaction analysis is done
correctly BUT wrong visualisation
technique is used.
Interaction analysis is presented
accurately with proper
visualisation technique BUT with
minor errors.
Masterful analysis of interaction effects
supplemented by a correct
visualisation.
10%
Either inappropriate
predictive model is
developed and/or analysis
lacks All steps of modebuilding process missing.
Relevant IVs are included
in the predictive model,
BUT some steps of modelbuilding process missing.
A predictive model is developed
with All model-building steps
included, BUT the final model is
incorrect and/or there are many
errors in the analysis.
An appropriate predictive model
is developed with All modelbuilding steps presented BUT
there are minor errors in the
analysis.
The final model includes those IVs
that have predictive power with
All steps in model-building
process clearly presented.
Model-building process is presented in
logical/comprehensive manner AND the
final model is correct.
Predicted probabilities are
not calculated and/or a
visualisation is missing.
Not All Predicted
probabilities are
calculated and/or a
visualisation is missing.
All predicted probabilities are
calculated and a visualisation is
presented BUT there are many
errors in the analysis.
All predicted probabilities are
calculated and a visualisation is
presented BUT there are minor
errors in the analysis.
All predicted probabilities are
calculated correctly and a proper
visualisation is presented.
A skilful and comprehensive analysis of
predicted probabilities is presented
along with a well-structured
visualisation.
10%
Does not use any
appropriate time-series
techniques.
Uses irrelevant or
inappropriate techniques
to analyse the time-series
and/or there are many
errors in the analysis.
A relevant time-series model
developed but there are many
errors in the analysis.
A relevant time-series model is
developed and but there are
minor errors in the analysis.
Time-series model is developed
correctly and relevant measure(s)
for evaluating the model quality
is presented.
Time-series model developed correctly
and presented in a clear and logical
fashion including relevant
visualisations.
Interpretation
& Technical
Report
GLO1
GLO2
GLO6
45%
Does not communicate any
of the main findings of the
analysis in an accurate or
meaningful way.
Interpretation and
communication of findings
is at a basic level or does
not adequately explain the
main findings of the
analysis.
Explains the main findings of the
analysis accurately and enables
reader to draw some reasonable
conclusions.
Provides an accurate description
of the most – but not all –
important features of the analysis,
with appropriate conclusions.
Provides very detailed and
accurate descriptions of the most
important features of the
analysis.
Provides an outstanding description
and conclusion of all relevant
analysis/visualisation outputs.
Interpretation of results are novel and
insightful.
5%
The technical report is
poorly structured and/or
few sections missing. Poor
use of technical language.
Does not consistently
The technical report is
poorly structured. Only
few analysis outputs are
presented in appendix.
Language is difficult to
The technical report is wellstructured with All required
sections included. Most relevant
analysis outputs are included in
appendix. Communication is not
The technical report is wellstructured with All sections
included. All relevant analysis
outputs are included in appendix.
Communication is clear with no
The technical reports on par with
a professional report. All relevant
analysis outputs are presented in
appendix in a logical order.
Written communication is clear,
The technical report is masterfully
structured. All relevant analysis outputs
are included in appendix. Outputs are
visually appealing, and follow a
MIS771 Descriptive Analytics and Visualisations Page 10 of 10
References
Kaggle 2017, IBM HR analytics employee attrition and performance, retrieved 02 July 2020,
demonstrate personal
autonomy or expert
judgement in contexts that
require self-directed work
and learning.
follow with many
grammatical errors noted.
Demonstrates very little
consistent personal
autonomy or expert
judgement in contexts
that require self-directed
work and learning
clear throughout the report and
grammatical errors noted.
Demonstrates some level
personal autonomy and expert
judgement in contexts that
require self-directed work and
learning
grammatical errors noted.
Demonstrates a good level of
personal autonomy and expert
judgement in contexts that
require self-directed work and
learning.
easy to follow and has a
structure.
Consistently demonstrates a high
level of autonomy and
authoritative judgement in
contexts that require selfdirected work and learning.
consistent formatting style. Language is
truly professional and easy to follow.
Consistently demonstrates an
exceptionally high level of autonomy
and authoritative judgement in
contexts that require self-directed work
and learning.
Assignment
planning and
execution
GLO6
15%
Takes no responsibility for
maintaining accurate
evidence of learning
achievements from within
formal course experiences.
Takes little responsibility
for maintaining accurate
evidence of learning
achievements from within
formal course
experiences.
Takes responsibility for seeking
improved learning and
maintaining evidence of learning
achievements from within formal
course experiences, although
there is some inconsistency in
application.
Consistently takes responsibility
for seeking improved learning and
maintaining evidence of learning
achievements from within formal
course experiences.
Consistently takes responsibility
for maintaining accurate and
detailed evidence of learning
achievements from within and
beyond formal course
experiences.
Consistently takes responsibility for
maintaining accurate and compelling
evidence of learning achievements from
within and beyond formal course
experiences.
OVERALL
0-29% 30%-49% 50%-59% 60%-69% 70%-79% 80%-100%
Fail (N) Pass (P) Credit (C) Distinction (D) High Distinction (HD)