MIS771 Descriptive Analytics and VisualisationBusiness Data Analysis MIS771 - Descriptive Analytics...

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MIS771 Descriptive Analytics and Visualisation

Business Data Analysis MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 1 of 10 MIS771 Descriptive Analytics and Visualisation Assignment Two Background This is an individual assignment, which requires you to analyse a given data set, interpret and draw conclusions from your analysis, and then convey your conclusions in a written technical report to an expert in Business Analytics. Percentage of final grade 35% The Due Date and Time 11.59 PM Sunday 12th May 2019 (AEST) 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 9th May 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. MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 2 of 10 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. For more information about academic misconduct, special consideration, extensions, and assessment feedback, please refer to the document Your rights and responsibilities as a student in this Unit in the first folder next to the Unit Guide of the Resources area in the CloudDeakin unit site. The assignment uses the file mdcb.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 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 limitation 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 suggested solutions will be released via CloudDeakin, usually within 15 working days. You are expected to refer and compare your answers to the suggested solutions to understand any areas of improvement. MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 3 of 10 Case Study (Background to Mad Dog Craft Beer) Mad Dog Craft Beer, is an Australian micro-brewery company with less than fifteen years of experience in brewing ale. Despite its limited operations in Melbourne and regional Victoria, the company has experienced fast growth in its production and sales in the past couple of years. In 2018, the company reported increasing its brewing capacity to 3 million litres per year to meet the increasing demand of its pale ale beer. Mad Dog Craft Beer sells pale ale to two market segments: a) pubs, bars and restaurants and b) bottleshops. Their beer is sold to these market segments either directly to the buyer or indirectly through a sales representative. Despite their successful operations and solid financial turnovers in the last two years, Mad Dog Craft Beer is forecasting a shift in business climate within the next five years. This is a result of the ever-increasing popularity of craft beer among Victorians and emergence of micro-brewery culture in this region. Now more than ever, Mad Dog Craft Beer management feels the need to ensure a strong relationship with its customer base. In addition, they are planning to put in place a formal procedure to forecast their beer production. This would help Mad Dog Craft Beer accurately project future supply and demand and adjust production needs accordingly. Subsequently, Mad Dog Craft Beer has approached BEAUTIFUL-DATA (a market research company) and asked them to conduct a large-scale survey of their clients to better understand the characteristics of Mad Dog Craft Beer’s customers, and their repurchase intention. Data Collection Process (Conducted by BEAUTIFUL-DATA) To address Mad Dog Craft Beer’s concerns, BEAUTIFUL-DATA has contacted Mad Dog Craft Beer’s clients and encouraged them to participate in an online survey. The collected data are then supplemented by other information compiled and stored in Mad Dog Craft Beer’s datamarts and accessible through its decision support system (DSS). Primary Database (accessible via mdcb.xlsx file) The primary database consists of 200 observations. There are three types of information in this database. The first group of information comes from Mad Dog Craft Beer’s data warehouse and includes information about the customer such loyalty in years, type, region, and distribution channel. The second group of information relates to the customers’ perceptions of Mad Dog Craft Beer on nine attributes. Mad Dog Craft Beer’s customers were asked to rate the company on nine attributes using a 1 – 10 scale. The third group of information relates to quantity ordered and business relationships (e.g., number of bottles bought from Mad Dog Craft Beer; and the likelihood of recommending Mad Dog Craft Beer to others). A complete listing of variables, their definitions, and an explanation of their coding are provided in mdcb.xlsx file. MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 4 of 10 Your Role as a BEAUTIFUL-DATA Data Analyst Intern You are a graduate student doing an internship at BEAUTIFUL-DATA. The research team manager (Todd Nash, with a PhD in Data Science and a Master Degree in Digital Marketing) has asked you to lead the data analysis process for the Mad Dog Craft Beer project and directly report the results to him. You and Todd just finished a meeting wherein he briefed you on the primary purpose of the project. Todd explained that a model should be built to estimate Order Quantity. Therefore, the first goal is to identify critical factors that influence the quantity ordered. Todd is also interested in gaining more profound insights into factors that predict the likelihood of current clients to recommend Mad Dog Craft Beer’s products to others. The final goal is to construct a model which forecast Mad Dog Craft Beer’s Pale Ale production in the upcoming four quarters. From these insights, Mad Dog Craft Beer will be in an excellent position to develop plans for the next financial year. Todd also allocated relevant research tasks and explained his expectations from your analysis in the meeting. Minutes of this meeting are available on the next page. Now, your job is to review and complete the allocated tasks as per this document. MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 5 of 10 BEAUTIFUL-DATA, 727 Collins St, Docklands VIC 3008 Phone: (+61 3 212 66 000) [email protected] Reference PH-102 Mad Dog Craft Beer Project Revised April 12, 2019 Level Expert Analysis Meeting Chair Todd Nash Date 12 April 2019 Time 11:00 AM Location BEAUTIFUL-DATA F3.101 Topic Mad Dog Craft Beer Project – Analytics Details Meeting Purpose: Specifying and Allocating Data Analytics Tasks Discussion items: • Variable(s) description. • Modelling Quantity Ordered. • Modelling the likelihood of recommending Mad Dog Craft Beer to others. • Forecasting Pale Ale production in the upcoming four quarters. • Producing a technical report. Detailed Action Items Who: Graduate Intern What: 1. Provide an overall summary of the following two variables: 1.1. Order_Qty 1.2. Recommend 2. Identifying potential factors that may influence Order_ Qty: 2.1. An appropriate statistical technique could be used here to identify a list of possible factors. 2.2. Build a model (through a model building process) to estimate the Order_ Qty. 2.3. Todd has done a separate regression analysis and found that the perception of beer quality is a significant predictor of the quantity ordered. In line with his findings, prior research shows that the strength of this relationship may vary according to brand image. That is, customers tend to associate the brand image with product quality. Therefore, Todd believes that the relationship between quality and quantity ordered should be stronger for those who have a more favourable perception of a brand. Your task here is to test Todd’s assumption by modelling the interaction between the predictors mentioned above and the target variable. Comment whether there is sufficient evidence that the interaction term makes a significant contribution to the model. MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 6 of 10 3. Finalise the model to predict

mis771assignment-2-t1-2019-pg1nmggm.pdf mdcb-q1ocixnu.xlsx

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Table of Contents
Introduction	2
Analysis	3
Task 1	3
Task 2	3
Task 2.1	3
Task 2.2	4
Task 2.3	6
Task 3	7
Task 3.1	7
Task 3.2	8
Task 3.3	10
Task 4	10
Conclusion	12
References	14
Introduction
The dependent variable is quantity ordered and recommendation. The independent variables are Loyalty, Customer Type, Region, Distribution Channel, Quality, SM Presence, Advert, and Brand Image, comp Pricing, Order Fulfilment, Flex Price, Shipping Speed, Shipping Cost, Order Quantity, and Recommend. The technique of descriptive statistics is used to analyse the order quantity and recommendation. The technique of correlation analysis can be helpful to identify the factors which affect the order quantity. A regression equation is constructed to predict the quantity ordered on the basis of identified factors. A logistic regression equation is created to predict the probability of recommendation. 
The dependent variable Pale Ale production (litres) is predicted for 2nd quarter 2019, 3rd quarter 2019, 4th quarter 2019, and 1st quarter 2020. The technique of regression analysis is used for prediction. 
Analysis
Task 1
The average number of bottles ordered by the customer is 7665 with a low standard deviation of 0.89 units. The distribution of bottles order by the customer is approximately normally distributed as skewness is equal to -0.2.

Business Data Analysis MIS771 - Descriptive Analytics and Visualisation Trimester 1, 2019 Page 1 of 10 MIS771 Descriptive Analytics and Visualisation Assignment Two Background This is an individual...

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