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Investigate the prices of second hand cars and the factors that may affect the price.

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I am going to investigate the prices of second hand cars and the factors that may affect the price.  These factors could come from a last list including mileage, engine size, age of vehicle, months of MOT and how much tax there is on the car.  I shall analyse this data so that I can decide which factor has the biggest affect on a car’s price.

I will use graphs and charts to help with my explanation.


From a list of 100 cars I decided to do a selective sample of 20%, so I chose every fifth car as this will be a good spread.  20% should be a good sample size because there will be a good spread of data and, therefore, I should get accurate results.  This will only be a rough test to see if the cars are close enough in similarity for me to draw any results from them (see appendix 1).  


“Age of car will affect price”

I thought age was one of the biggest factors that would affect car prices.  First of all I calculated the % depreciation of all the cars in my sample.  I calculated the % rather than straight monetary loss because it helps smooth out the extremes between car makes eg Bentleys and Ford. I did a scattergraph (appendix 2) of age against % depreciation and then drew a line of best fit.

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“The estimated mode for car manufacturers in my sample is Ford.”

I will use a tally chart to record my results.

































Again, my hypothesis was proved wrong, as Vauxhall is the most frequently sold second hand car along with VW.


“Make and age of car will influence price”

I will now calculate the mean average second hand cost of Vauxhall and VW cars in this sample to see whether they have similar prices.  I am only using these two makes of car as they are the modal makes of car in my sample.

(figures in £ sterling)


400 + 3695 + 7550 + 4693 = 16338 / 4 = 4084.50


7499 + 1000 + 4976 + 3191 = 16666 / 4 = 4166.50

There is a very strong link between the prices for these two makes of car since the price difference is only £82.  This suggests that the make of middle range cars does not affect prices of second hand cars.  However, we do have one extreme in the VW of £400.

I will now investigate the average of these cars to see if there is any link between price and the age at which they are sold.


15 + 7 + 1 + 5 = 28 / 4 = 7


4 + 10 + 4 + 6 = 24 /  = 6

So, I can come to the conclusion that 1 year’s difference between these two cars average makes £82 difference in price.  This is a small difference, but again the low price and high age of one of the VW cars could be distorting the results.

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My second hypothesis was that cars with short MoTs are frequently sold.  The cumulative frequency showed the interquartile range to be between 2 and 9 months left on the MoT.  It was difficult to interpret the results on this as 3 cars in the sample did not have to have MoTs as they were under 3 years old.  This makes the data unreliable so I cannot draw a reliable conclusion for this hypothesis.

My third hypothesis concerned mileage.  I was surprised to find that second hand cars are most likely to have only a mileage of 30,000.

My fourth hypothesis showed that Vauxhall is the most frequently available second hand car.

Hypothesis 5 looked to see if make and age of car would influence price.  I found that the make of car did not seem to influence the second hand value as strongly as I would have thought.  There was a strong correlation between these sets of data.

Hypothesis 6 suggested that cars, selected at random, there was a 0.43 probability of this being true.  This is a strong correlation when considering all the different colours of car available.

It is likely that a buyer will want about 4 months of tax on a second hand car.

Whilst there were some strong correlations, particularly that between age of car and price, the histogram showed that my sample might be too small to predict the true influences on prices of second hand cars.

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