• Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

I have been asked to investigate factors that affect the depreciation of cars.

Extracts from this document...


I have been asked to investigate factors that affect the depreciation of cars.  To do this I would ideally like to collect my own data about used cars.  This would be called primary data.  I would have collected data on the make, model, mileage, engine size, age, price and price when new of several hundred used cars.  Unfortunately this would have taken a lot of time, but the advantage would have been that it would have been reliable data which I could trust, and I could have found out exactly the information that I wanted.

        It would have been impossible for me to do such a large survey, however, so I had to use secondary data that I got from the CCEA website.  The advantage of this was that it was quick, cheap and easy, but I can’t be sure of the accuracy of these results and I don’t know if any bias was involved when it was being collected.  I have also found that many of the results are incomplete.

        From the very start, I am sure that two of these results are wrong – a Renault Laguna which costs £50,000, and a Renault Clio that increases in value.  I have deleted these results straight away.

Hypothesis 1

My first hypothesis is that cars depreciate more as they get older.  I used the spreadsheet on the computer to test this hypothesis, but first I had to get the age and percentage depreciation for each car, neither of which are recorded in the table.

        Firstly, to get the age of the cars, I subtracted the year in which they were made from 2002, the year when the data was collected.  I first created a new column on the spreadsheet and called it age.

...read more.


Cars taken =  (Cars in group / Total no of cars) x 40

When worked out to the nearest whole number, group 1 will have 14 cars selected from it, group 2 will have 17 cars selected from it, and group 3 will have 9cars selected from it.  I will use the random number generator on my calculator to select the cars at random as follows.  

Place in group of car selected = Random number x No.  of cars in group

I will use this formula and select the nearest whole number to my answer until I have selected the required number of cars from each group.  When I have selected the 40 random cars, I will plot their mileage against their percentage depreciation in a scattergraph. (See scattergraph)

The graph shows a definite positive correlation, meaning that as the mileage of the car increases, so does the percentage depreciation.  This time the best-fit line is not curved, suggesting that the two values are directly proportional – as mileage doubles, so does percentage depreciation.  There is again a large variation in the percentage depreciation, as the values form a wide band across the axis.  This is due to the effect of age on the percentage depreciation.

        These results agree with my hypothesis and show that the mileage must have an effect on the percentage depreciation.

        This graph can also be used to make predictions about used cars.  For example, I have found an advertisement for a 1.2 litre ford fiesta made in 1999.  All of the 1.2 litre fiestas made in 1999 on the spreadsheets were priced at £9,020, so I will assume that this was the original price for the car in question.

        The advertisement says that the car has a mileage of 34,000 miles, and states a price of £2,100.

...read more.



I found that make had a small effect on the percentage depreciation.  I found that Vauxhalls depreciate the most and that Peugeots depreciate the least.  However, the difference was less than 20%, so make has a smaller effect on the depreciation than age or mileage.  


I found that the fuel on which a car runs has a small effect on the depreciation on the car.  I found that diesel cars depreciate more than petrol cars do.  However, the difference was very small - less than 7% - and I was unable to determine whether this difference was caused by the fuel used or by bias involving age in the original sample.

All of the data that I have collected can be used to make decisions when buying a new or used car.

        If I was buying a new car, I would buy a Peugeot that runs on petrol, because the data suggests that this sort of car would be likely to depreciate the least, and therefore be worth more when the time came to sell it.  I would definitely not buy a Vauxhall as they depreciated more than the other makes in the survey.

        If I was buying a used car, I would buy a Vauxhall, because they have depreciated more than any other type of car in the survey and should therefore be the cheapest.  I would try to buy one that was less than 5 years old, however, because of the way that the graph of depreciation against age curves.  After 5 years old, the slope of the graph becomes very gentle.  This means that the cars are getting older, and therefore in worse condition, but the price is falling very slowly, so you will get less value for money as the cars get older.

...read more.

This student written piece of work is one of many that can be found in our GCSE Gary's (and other) Car Sales section.

Found what you're looking for?

  • Start learning 29% faster today
  • 150,000+ documents available
  • Just £6.99 a month

Not the one? Search for your essay title...
  • Join over 1.2 million students every month
  • Accelerate your learning by 29%
  • Unlimited access from just £6.99 per month

See related essaysSee related essays

Related GCSE Gary's (and other) Car Sales essays

  1. Used Cards - find which factors will influence the price of a second hand ...

    BMW 3-Series 91-99 12825 68000 6 31 Skoda Fabia 3585 20000 7 34 Mazda 121 1620 55000 10 43 Mazda Demio 1920 71000 9 70 Daihatsu Sirion 4915 17500 5 71 Mercedes Cab E-Class 10920 9500 10 75 Ford Mondeo 96-00 3335 22000 8 79 Subaru Forester 4550 50000 10

  2. T-Total Maths

    14 4 24 13 14 15 5 25 14 15 16 6 26 15 16 17 7 27 16 17 18 8 28 17 18 19 9 29 18 19 20 10 30 5 X 11 =55 - 62 = -7 FORMULA = 5N +7 USING ALGEBRA IN T SHAPE

  1. Statistics: Factors Affecting the Price of Used Cars

    can't find the mode for the mileage and price because they are all different. Both the mean and median year is 2004 if the mean is rounded off. The price mean and median is quite close together with around a 300 difference.

  2. Maths Coursework - Used Cars

    Ford Price of car (�) Frequency Class Width Interval Frequency density 0<P?2000 6 2000 0.003 2000<p?5000 4 3000 0.0013 5000<p?9000 3 4000 0.00075 Fiat Price of car (�) Frequency Class width Interval Frequency density 0<P?3000 5 3000 0.002 3000<p?5000 2 2000 0.001 5000<p?6000 1 2000 0.0005 6000<p?7000 0 1000 0 Rover Price of car (�)

  1. Maths - statistical driving test

    These identified factors which are uncontrollable will have an effect on the students performance, in return having a knock on effect on the instructors achievement of teaching students that make less mistakes. Further Investigation While investigating my second hypothesis I came to the conclusion that instructor C was the best

  2. Maths Coursework:Used Cars

    First of all I will do the mileage for all the four car makes and then do mileage for the four cars separately. I am investigating whether the mileage has an effect on the second hand price. Mileage This graph has a steep, negative correlation and it shows that it loses �0.0724 per year.

  1. Maths Coursework: Car Sales

    Therefore I will conclude that the modal was a success for each brand and also the one that was the average for all the brands. Conclusions: Here is what I have found out in this investigation about the selling used cars and is done in bullet points: * Very good

  2. Used car prices.

    This shows that my prediction was correct and as there are hardly any cars with a very high mileage and there are only a few with smaller mileages. I will now produce another histogram again with the mileage data of the car make of Vauxhall.

  • Over 160,000 pieces
    of student written work
  • Annotated by
    experienced teachers
  • Ideas and feedback to
    improve your own work