Bivariate Data Exploration

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Bivariate Data Exploration

Aim:

The aim of this investigation is to see if there is a correlation

between the engine size of a car and the insurance group that it

resides in.

Introduction:

In our present day there is an ever-increasing public demand for

value-for-money products and services, especially in cars, shopping

and clothing markets. For students, this is even more important as

everything they buy (unless they are particularly affluent) can easily

amount to debt (through extensive student loans). For students in

particular, cars are very often an essential means of transport, and

so, like most things, it is important for a student to get the best

deal for their car.

However, insurance companies and car dealers are very much aware of

the student situation and have classified certain cars as ‘student

carsÂ’, and to clarify this, include cars from Peugeot (106, 306),

Renault (Clio), Citroen (Saxo), and Vauxhall (Nova) to name but a few.

Now it seems that these cars all have relatively low engine sizes,

commonly ranging from 900-1800cc, and are all placed in relatively low

insurance groups (and therefore have lower insurance costs), but this

may not be the case for all cars, especially those with larger engine

sizes.

This investigation will examine data from a range of cars, varying in

both engine size and insurance group, and if a positive correlation is

found between insurance group and engine size, then the concept of

‘student cars’ will not be such a worrying factor when a student goes

to buy his first car, however if there is no correlation then it is

entirely possible that insurance companies are charging too much for

cars in the ‘student car’ category.

Data Collection:

To start the investigation, data needed to be collected before any

conclusions could be made. A local used car showroom was approached,

and data from all cars on their forecourt (and in the showroom) was

collected. This data was taken directly from the records that the

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