Create Data Driven Fact Based Solutions - Business Assignment Help

Assignment Task :

Learning Outcomes :

 1: Apply quantitative reasoning skills to analyse business problems.
 2: Create data-driven/fact-based solutions to complex business scenarios.
 3: Implement contemporary data analysis tools to analyse business performance.
 4: Interpret findings and effectively communicate solutions to business scenarios.

General Description / Requirements
The assignment requires that you analyse a data set, interpret, and draw conclusions from your analysis, and then convey your conclusions in a written email. The assignment must be completed individually and 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 hard copies or assignments submitted via email.
The assignment uses the file MIS171_A2.xlsx which can be downloaded from CloudDeakin. The assignment focuses on materials presented up to and including Week 6.

Scenario
You are Priya Acharya, and you work as an analyst for a financial institute named Financero1 , which provides its customers with a range of financial services including retail, business and institutional banking, funds management, and investment. Financero decided to conduct research on the saving and shopping habits of its customers. The provided data set includes a random sample of 200 businesses.
You have received an email from the Director of Analytics (Boris Johns), that contains specific questions that you must answer.

Part 1: Data Analysis 
When conducting the analysis, you will apply techniques from descriptive analytics, visualisations, probabilities, hypothesis testing, and confidence interval calculation. Hence, you will use various tables, graphs, and summary measures. When exploring data, we often produce more results than we eventually use in the final report, but by investigating the data from different angles, we can develop a much better ‘feel’ for the data: a deeper understanding of the data. Always ensure that
you consider relevant modelling assumptions.
The analysis section you submit should be on Q1 to Q6 sheets of the Excel file. Where possible, it is always useful to produce both numerical and graphical statistical summaries as sometimes, something is revealed in one that is not obvious in the other. Your analysis should be clearly labelled and grouped around each question. Poorly presented, unorganised analysis, or excessive output will be penalised.

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