Please see the PDF files for the questions.
Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 10 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. https://www.deakin.edu.au/students/faculties/buslaw/student-support/assignment-extensions MIS771 Descriptive Analytics and Visualisations Page 2 of 10 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. MIS771 Descriptive Analytics and Visualisations Page 3 of 10 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. MIS771 Descriptive Analytics and Visualisations Page 4 of 10 Form 210-3 ANALYTICS7 Team Meeting ANALYTICS7 727 Collins St, Docklands VIC 3008 Phone: (+61 3 212 66 000)
[email protected] 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. MIS771 Descriptive Analytics and Visualisations Page 5 of 10 Therefore, Hugo believes that the relationship between performance rating and percentage increase in salary should be stronger for employees who