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Assignment-1 Descriptive Analytics and Visualisations Page 1 of 9 MIS771 Descriptive Analytics and Visualisation 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 conclusions using plain language in a written report to a person with little or no knowledge of Business Analytics. Percentage of the final grade 30% The Due Date and Time 11.59 PM Sunday 18th August 2019 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. No extensions will be considered unless a written request is submitted and negotiated with the unit chair before Thursday 15th August 2019, 5:00 PM. Please note that assignment extensions will only be considered if you attach your draft assignment with your request for an extension. You must keep a backup copy of every assignment you submit (that is, the work you have done to date) until the assignment has been marked. In the unlikely event that an assignment is misplaced, you will need to submit your backup copy. Work you submit will be checked by electronic or other means to detect collusion and/or plagiarism. When you submit an assignment through your CloudDeakin unit site, you will receive an email to your Deakin email address confirming that the assignment has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after upload, and check for, and keep, the email receipt for the submission. 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 up to five days, and work that is submitted more than five days after the due date will not be marked. You will receive 0% for the task. 'Day' means calendar days or part thereof. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date. 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. Descriptive Analytics and Visualisations Page 2 of 9 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. GLO3: Digital Literacy - Using technologies to find, use and disseminate information GLO5: Problem Solving - creating solutions to authentic (real world and ill-defined) problems. ULO 1: Apply quantitative reasoning skills to solve complex problems. ULO 2: Use contemporary data analysis and visualisation tools and recognise the limits of such tools. 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. Descriptive Analytics and Visualisations Page 3 of 9 Case Study You are Natalia Navarska, 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 are the increasing numbers 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 are advertising that they can save vehicle owners hundreds of dollars each year on insurance premiums. Just recently, your research and analysis group secured a dataset from the Insurance Brokers Association (IBA), which is a random sample of 400 customers who obtained the services of car insurance brokers. You have performed an exploratory analysis and have emailed the results (see pages 6-7) to Edmond Kendrick, one of the senior editors of Financial Review Magazine. Edmond has replied to your email regarding the Insurance Brokers. His email is reproduced next page: Descriptive Analytics and Visualisations Page 4 of 9 Email from Edmond To: Natalia Navarska From: Edmond Kendrick Subject: Analysis of car insurance brokerage services Hi Nat, Thank you for the comprehensive analysis and notes. Now I am more curious about what else could we learn from analysing the dataset. 1. From what I can gather from your notes, iChoose was able to save their customers more money than other brokers. Can I now conclude that iChoose, on average, can save more on insurance premiums than uChoose? 2. Your analysis of 400 customers showed that the proportion of dissatisfied (i.e. either ‘Dissatisfied’ or ‘Very Dissatisfied’) urban customers is smaller than the proportion of dissatisfied rural customers. Can we argue that this difference would hold across all urban and rural customers? 3. I did my own analysis of the sample and came to the following conclusions: a. The average savings on insurance premiums differ between rural and urban customers. b. On average, customers with ‘Agreed Value’ policy saved more on their insurance premiums than the customers with ‘Market Value’ policy; c. The proportion of female customers with a diamond level no claim bonus rating (NCBR) is less than male customers with a diamond level no claim bonus rating (NCBR); What would be great is if you can verify my findings and tell me how much the difference is in each of the three scenarios mentioned above. 4. I would like you to expand the analysis and look at whether: a. The average savings on insurance premiums significantly differ between Victoria, NSW and Queensland. b. The average savings on insurance premiums significantly differ between 4WD, Luxury and Sports car. 5. Does the proportion of customers who approached their insurance provider before reaching out to a broker differ between the insurance providers? 6. I asked Raj to design an experiment to see the effects of the valuation method and the vehicle type on savings on insurance premiums, he sent me a table with some numbers (see Appendix- A). Can you complete the analysis? I look forward to your response. Regards Eddie Descriptive Analytics and Visualisations Page 5 of 9 Appendix- A: Data for the experiment prepared by Raj Valuation Method 4WD Family Sport Luxury Agreed Value 1068 169 1799 966 128 150 680 1144 98 -59 373 893 560 22 143 1144 429 108 442 629 Market Value 104 54 99 1273 72 0 156 247 311 94 1084 357 146 84 357 676 135 -10 131 366 Vehicle Type Descriptive Analytics and Visualisations Page 6 of 9 An Extract of the Analysis and Notes Prepared by Nat • A summary of Savings: Savings Mean 229.64 Standard Error 16.03 Median 113 Mode 0 Standard Deviation 320.56 Sample Variance 102759.59 Kurtosis 5.46 Skewness 2.08 Range 2043 Minimum -87 Maximum 1956 Sum 91857 Count 400 Q1 12 Q3 357 IQR 345 LF -505.5 UF 874.5 OUTLIERS YES • Summary of Saving by Broker (Broker Performance) iChoose uChoose vChoose yChoose Mean 262.442 230.847 137.381 204.188 Standard Error 25.883 36.672 14.330 31.575 Median 127 94.5 123.5 100 Mode 0 0 294 0 Standard Deviation 356.766 311.169 92.868 309.368 Sample Variance 127281.930 96825.934 8624.437 95708.659 Kurtosis 4.121 4.678 -0.461 6.102 Skewness 1.826 1.934 0.442 2.210 Range 2034 1645 392 1738 Minimum -78 -69 -31 -87 Maximum 1956 1576 361 1651 Sum 49864 16621 5770 19602 Count 190 72 42 96 Q1 0 24 65.5 0 Q3 412.5 388.75 200 338 IQR 412.5 364.75 134.5 338 LF -618.75 -523.125 -136.25 -507 UF 1031.25 935.875 401.75 845 OUTLIERS YES YES NO YES Saving Outcome Count of Customers Not benefited from (saving < 0)="" 72="" neither="" benefited="" nor="" lost="" (saving="0)" 25="" benefited="" from="" (saving=""> 0) 303 0 20 40 60 80 100 120 140 160 180 Fr eq ue nc y Saving ($) HISTOGRAM: SAVING Descriptive Analytics and Visualisations Page 7 of 9 • Customer Satisfaction Customer Satisfaction Count of Customers Very Dissatisfied 35 Dissatisfied 57 Satisfied 174 Very Satisfied 134 Total 400 • Customer Satisfaction by Area Satisfaction Area Very Dissatisfied Dissatisfied Satisfied Very Satisfied Total Rural 10 23 32 30 95 Urban 25 34 142 104 305 Total 35 57 174 134 400 Notes to Edmond Savings: From a sample of 400 customers, • On average, car insurance brokers saved their customers $113 (median). • The middle 50% of customers saved between $12 and $357; a quarter of the customers saved at most $12; three-quarter of the customers saved no more than $357. • The savings ranged from a loss of $87 to a substantial gain of $1956. • Almost 40% of the customers, saved between $1 and $200 on their current insurance premiums; car insurance brokers have shown their ability to find an appropriate policy for most of their customers. • The bulk of the customers have relatively low (in few cases none at all) annual savings on premium, with a relatively small number having high savings. 89% of