MIS171: Leonard’s - Korean Restaurant - Report Writing - Business Analytics Assignment Help

Assignment Task:

Unit Learning Outcome

ULO1: Apply quantitative reasoning skills to analyse business problems. 

ULO2: Create data-driven/fact-based solutions to complex business scenarios.

General Description / Requirements 

This is an individual assignment and it focuses on materials presented up to and including Week 9. 

To complete the assignment you should: 

• analyse the dataset to answer the specific questions presented to you during the online test (see the scenario below); 

• interpret the results, and draw conclusions. 

Once you have done this, you will have the necessary output (i.e., data analysis, related summary information, and relevant visualisations) to complete the online test. The test contains fifteen (15) randomly allocated multiple-choice questions related to your data analysis, its interpretation, and your subsequent conclusions. The assignment uses the file MIS171-2020-T1-A3.xlsx which can be downloaded from CloudDeakin.

Scenario 

The Leonard’s is a Korean restaurant chain founded in 2017 that specialises in Korean fried chicken and beer. Leonard’s has three branches in Australia (Melbourne, Sydney, and Canberra). Their product range includes different types of fried chicken, Korean rice, burgers, salads, beer and beverages, and desserts. 

You are Anthony Mcpel, a business analyst who works for the Leonard’s in their Research and Analysis department. You have received an email from the Head of Analytics, Nimish Rayan, asking to perform some data analysis on a dataset provided to you. The email making this request alongside some guidelines (in blue) is presented below. Consider these comments carefully.

1. Project 1: Leonard’s customers’ spend analysis 

The data set includes a random sample of 400 Leonard’s customers. Build a multiple regression model to predict the amount spent per transaction. Your model should provide insights into what factors influence spent amount per transaction as well as the ability to predict spent per transaction for various scenarios. 

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