Create a data-driven presentation highlighting your findings

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Reference no: EM133913608 , Length: 10 slides

Data Visualisation and Communication

Assessment - Insights on Car Theft for Insurance Premiums

Task

You are part of a Business Analytics team at a New Zealand-based car insurance company. Your team has been asked to analyse a dataset to identify risk factors associated with vehicle theft across New Zealand. Using your findings, you will prepare a presentation that highlights key indicators and actionable insights to help decision-makers adjust insurance premiums in line with identified risks.

Assessment Description

To complete this task, your team will:

Formulate and test 3 hypotheses based on the data. Each hypothesis should make a specific, testable claim (e.g. "SUVs are more likely to be stolen in urban areas") and be supported by visualisations and statistics from your analysis. Get top-rated assignment help now.

Evaluate the limitations of the dataset and how they affect the strength of your conclusions.

Create a data-driven presentation highlighting your findings. Your presentation should include key points summarising risk patterns (e.g. certain locations or vehicle types linked to higher theft rates), and explain how these can inform premium adjustments.

Assessment Instructions

Form groups of 3-4. If you do not have a group your teacher will allocate you to a group in class. During class in week 8 you will be required to create a PowerPoint presentation with your hypothesis. At the end of class you will upload your presentation to Turnitin on MyKBS. There is no need to verbally present your slides to your fellow students.

Nature of Data Set

The data set to be provided contains the following fields:

Spreadsheet 1
location_id = where the vehicle was stolen from region = the region where the vehicle was stolen country
population = population in the region density= population / land area

Spreadsheet 2 make_id
make_name = name of vehicle make_type = standard or luxury

Spreadsheet 3 vehicle_id
vehicle_type = type of vehicle make_id
model_year = year that vehicle was built vehicle_desc = description of vehicle color = colour of vehicle
date_stolen = date the vehicle was stolen location_id

The spreadsheets have unique identifiers that will allow you to combine spreadsheets in your analysis, namely location_id and make_id

An example of an analysis may include types of vehicles that are more likely to be stolen in particular location.

Reference no: EM133913608

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