I need this assignment very precise and high quality. Give me the best priceI am giving first time in thanksforthe help website
Assignment-1 MIS771 Descriptive Analytics and Visualisations Page 1 of 7 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 This is an individual assignment. You need to analyse a given data set, 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 11.59 PM Wednesday 15th April 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 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 in 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 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 serious 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 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, victim of crime. Note: misreading the due date, assignment anxiety or returning home will not be accepted as grounds for consideration. https://www.deakin.edu.au/__data/assets/pdf_file/0006/2055552/BL_AssignmentExtensionForm_Feb2020.pdf MIS771 Descriptive Analytics and Visualisations Page 2 of 7 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 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 A1.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. MIS771 Descriptive Analytics and Visualisations Page 3 of 7 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 4 of 7 The Case Study According to a study published in the US News and World Report in 2010, the cost of medical malpractice in the United States is $55.6 billion a year, which is 2.4 per cent of annual health-care spending*. Another 2011 study published in the New England Journal of Medicine revealed that annually, during the period 1991 to 2005, 7.4 per cent of all physicians licensed in the US had a malpractice claim against them. These staggering numbers not only contribute to the high cost of health care in the US, but the size of successful malpractice claims also contributes to high premiums for medical malpractice insurance. A report from McKinsey (May 2014)† Unleashing the Value of Advanced Analytics in Insurance states: "The proliferation of third-party data sources is reducing insurers' dependence on internal data. Digital "data exhaust" from social media and multimedia, smartphones, computers, and other consumer and industrial devices — used within privacy guidelines and assuring anonymity — has become a rich source for behavioural insights for insurance companies, as it has for virtually all businesses. Recently, the release of previously unavailable or inaccessible public sector data has greatly expanded potential sources of third-party data. The US and UK governments and the European Union have recently launched "open data" Web sites to make available massive amounts of government statistics, including health, education, worker safety, and energy data, among others. With much better access to third-party data from a wide variety of sources, insurers can pose new questions and better understand many different types of risks." The UnitedHealth Group: America's most prominent health insurance provider has collated a range of data and wants to develop a better understanding of its claims paid out for medical malpractice lawsuits. Its records show claim payment amounts, as well as information about the presiding physician and the claimant for many mediated or settled lawsuits this year. You are a Data Analyst working for UnitedHealth Group. Your Manager – Edmond Kendrick has asked you to conduct a preliminary analysis of collected data. In particular, you are expected to perform a series of descriptive and inferential analyses and produce a report based on your findings. The data set contains numerous variables and details about the claims. The eight variables in the data table are described below: Claimant ID Unique ID of the claimant Amount Amount of the claim payment in dollars Severity The severity rating of damage to the patient (MILD, MEDIUM, SEVERE) Age Age of the claimant in years Private Attorney Whether the claimant was represented by a private attorney Marital Status Marital status of the claimant Specialty Specialty of the physician involved in the lawsuit Insurance Type of medical insurance carried by the patient Gender Patient Gender Edmond's email to you is reproduced on the next page. * https://www.hsph.harvard.edu/news/press-releases/medical-liability-costs-us/ † https://www.mckinsey.com/industries/financial-services/our-insights/unleashing-the-value-of-advanced- analytics-in-insurance https://www.hsph.harvard.edu/news/press-releases/medical-liability-costs-us/ https://www.mckinsey.com/industries/financial-services/our-insights/unleashing-the-value-of-advanced-analytics-in-insurance https://www.mckinsey.com/industries/financial-services/our-insights/unleashing-the-value-of-advanced-analytics-in-insurance MIS771 Descriptive Analytics and Visualisations Page 5 of 7 Email from Edmond Kendrick To:
> From: Edmond Kendrick Subject: Analysis of Claims (updated) Hi, As discussed earlier, I have cleaned and simplified the dataset to eight variables for your convenience. The cleaned dataset contains information about 200 randomly selected claims made this year. 1. I would like to compare this year's claims data against several other studies. a. Is there a difference in proportion of "MILD" or "MEDIUM" claims by a patient, 's Gender? Can we conclude that there is a difference in the proportion of "MILD" or "MEDIUM" type severity claims by female patients compared to that of male patients? b. As an industry standard, it is believed that the payment amounts are related to whether or not a private attorney represented the claimant. In particular, the average claim amount when a private attorney is involved is higher than when there is no private attorney involved. Does the data support this proposition? c. Also, the industry stakeholders believe that private attorney representation is higher for 'SEVERE' claims than for claims with a "MEDIUM" severity. Is this a valid statement? 2. The Insurance company is particularly concerned in 'SEVERE' claims as the amount of claims are significantly higher compared to other claims. Therefore, I would like to get an understanding of the relationship between the speciality of the physician involved; the severity of the claim, and the average claim amounts. a. I believe that the percentage of "SEVERE" claims with the