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Maths Data Handling-Secondhand Car

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        Maths: Data Handling Coursework

Second Hand Car Sales


        When selling a car, there are many factors that will affect the price of the car including:

Age, Mileage, Condition, Model, Engine size, Colour, Interior, Original Cost, Previous owners, Fuel/Petrol type, installed systems (e.g. Safety systems, sound systems, central locking), Air conditioning, Insurance, Tax and MOT, Number of seats, Type of Transmission, Miles per Gallon and Service History.

        I shall use only cars which are 2nd hand in my coursework because as soon as the car has been used the value instantly loses the VAT and becomes 2nd hand.


I shall look at Scatter Graphs for Price against Age, Price against Colour, Price against engine Size and Percentage depreciation against age. I shall also try to refine my graphs and where possible use secondary sources, I shall also look at the Percentage depreciation per year of certain models. I shall work out formulas for my trend lines, describe correlations and compare graphs where it is relevant. If I were to do these by hand I would select maybe 10 from my sample but because I have access to the spreadsheet, I can easily use all 100.


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Through this graph, we can see a weak but positive correlation which proves my prediction to be correct. I shall now try to use only a certain model which should eliminate those outliers from cars which had an expensive original price or those which have lost value much faster than the others.


Usingjust Fords, I have made a scatter graph to show how much more the sample has been refined and how much stronger the correlation gets when you refine the sample, without any proper outliers, this graph has a positive correlation.

Although this graph has a strongish correlation I do not believe that this is relevant because it uses the 2nd hand price which will be largely subject to the age rather than the engine size, I believe it would be more relevant if I took the original prices against engine size and than used the depreciation to relate it to the 2nd hand price.


Again by using just Fords, I have made a graph which clearly shows a positive correlation but there are too many outliers to make a formula. So I would need to choose a specific model to try to make a formula.

Age against Price:


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nd hand car.

Fords: Using the same method as I used before I have acquired the formula, Percentage Depreciation = 5 × Age + 35so the depreciation per year is about 5%. Also Fords lose 35% of their value after they have been used.

Vauxhalls: Again I shall use the same method to acquire the formula, Percentage Depreciation = 7.5 ×Age + 22.5 so the depreciation per year is about 7.5% and Vauxhalls lose 22.5% of their value after they have been used.


From this investigation, I have found that the price is mainly affected by the age and original price of the car although the make, model and peripherals also affect the price although they affect the original price which in turn affects the 2nd hand price. I am now also able to find out which cars lose value faster and I am also able to apply this in a business if I were to choose such a profession.

Although I was unable to use secondary sources because of information which was not included in the information (mainly original price), I was still able to use information in the sample I had to refine my graphs so I could have definite results. I have learnt many things from this investigation, mainly what affects the price of 2nd hand cars most and this may in future influence my choice of car which I plan on buying.

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