Descriptive analytics and visualisation
Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 6 MIS771 Descriptive Analytics and Visualisation DEPARTMENT OF INFORMATION SYSTEMS AND BUSINESS ANALYTICS DEAKIN BUSINESS SCHOOL FACULTY OF BUSINESS AND LAW, DEAKIN UNIVERSITY Assignment One Background Assignment one is an individual assignment with three tasks. The first task is to report on how to plan and deliver the assessment task on time. The second task is to analyse the given dataset and then interpret and draw conclusions from the analysis. Finally, the third task is to convey the findings and conclusions in a written report to a business professional with very little or no knowledge of Business Analytics. Percentage of the final grade 30% The Due Date and Time 8.00 PM Thursday 19th August 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 deadline. 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 (PDF, 188.6KB). Please make sure you attach all supporting documentation and a draft of your assignment. This 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 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, serious 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 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 returning home will not be accepted as grounds for consideration. Information for students seeking an extension AFTER the due date 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 https://www.deakin.edu.au/students/faculties/buslaw/student-support/assignment-extensions https://www.deakin.edu.au/students/faculties/buslaw/student-support/assignment-extensions MIS771 Descriptive Analytics and Visualisations Page 2 of 6 or extension due date. Additionally, 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 assignments submitted before any 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 considerations, 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 T22021A1.xlsx, which can be downloaded from CloudDeakin. Analysis of the data requires the use of techniques studied in Module-1. 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. MIS771 Descriptive Analytics and Visualisations Page 3 of 6 The Case Study for Data Analysis and Business Report You are a data analyst in the Research and Analysis group at Financial Review Magazine. Your primary role is to evaluate new products and services. You are often required to report outcomes of your analysis to senior editors at the Magazine who have little or no knowledge of data analysis. Of specific interest to Financial Review magazine is the increasing number of companies that offer brokerage services for car insurance and potentially what this means for consumers. An insurance broker is an independent insurance agent who works with many insurance companies to find the very best available policies for his or her customers. Most of these brokers advertise that they can save vehicle owners hundreds of dollars each year on insurance premiums. Your research and analysis group recently secured a dataset from the Insurance Brokers Association (IBA). It is a random sample of 400 customers who obtained the services of car insurance brokers. Your Manager – Edmond Kendrick, has asked you to analyse the collected data. In particular, you are expected to perform a series of descriptive and inferential analyses and produce a report based on your findings. Edmond's email to you is below. To:
> From: Edmond Kendrick Subject: Analysis of car insurance brokerage service Hi, As discussed earlier, I got one of the IT colleagues to clean and simplify the dataset for your convenience. The cleaned dataset contains information about 400 randomly selected customers, and I have the following questions/issues relating to the insurance brokers data. 1. Do female drivers under 30 save more on car insurance premiums than their male counterparts, on average? 2. Is the proportion of dissatisfied urban customers smaller than the proportion of dissatisfied rural customers? 3. Does the average savings on car insurance premiums differ across the two valuation methods? 4. I would also like you to analyse whether: a. The average savings on insurance premiums significantly differ across NSW, Victoria, and Queensland. b. The proportion of satisfied customers differ across the insurance brokers. 5. I would like you to design and run an experiment to see the effect of the valuation method and the vehicle type on savings on insurance premium using the data set in the attached Excel File – use Data in the "Experiment" worksheet. I look forward to your response on or before 19th August 2021. Sincerely, Eddie MIS771 Descriptive Analytics and Visualisations Page 4 of 6 SUBMISSIONS The assignment consists of three parts: Assignment Planning and Execution Tables, Analysis and Report. You are required to submit all three (your plan, data analysis and written report). 1. Guidelines for Assignment Planning and Execution Tables 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 self-management skills. Note: Dot point writing requires you to use 'point form', not complete sentences. 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 the Language and Learning Adviser. Note: Give the assignment planning and execution file the following name A1_Planning_YourStudentID.docx 2. Guidelines for Data Analysis Read the case study and questions asked by Edmond carefully. Then spend some time reviewing the data to get a sense of the context. The analysis required for this assignment involves material covered in Module 1, with the corresponding tutorials being a useful guide. The analysis should be submitted in the appropriate worksheets in the Excel file. Each question from the email should be analysed in a separate tab (e.g. Q1, Q2 … or Q3.1, Q3.2 …). You need to add these extra tabs. Before submitting your analysis, make sure it is logically organised, and any incorrect or unnecessary output has been removed. Marks will be penalised for poor presentation or disorganised/incorrect results, or any unnecessary output. For all questions in the email, you can assume that: • 95 % confidence level is appropriate for confidence intervals and; • 5.0 % level of significance (i.e. α = 0.05) is appropriate for any hypothesis tests. You can complete all data analysis using the Excel templates provided in practicals. In choosing the technique to apply for a given question, keep the following