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# Find out the factors that most affect the prices of second hand cars.

Extracts from this document...

Introduction

Maths Coursework

Hypothesis

For my maths project I will hope to find out the factors that most affect the prices of second hand cars. From my preliminary work I have chosen five factors that could possibly affect the prices the most. The five that I have chosen are:

1. Mileage
2. Age
3. Owners
4. Insurance Group
5. MPG (Miles Per Gallon)

Evidently these factors will all be compared to the percentage decrease in price from new to second hand. By the end of the project I will hopefully have found the biggest factor in percentage decrease in price. In this project I will be using several different graphs including correlation coefficient, scatter graphs and histograms. For my scatter graphs I will use the mileage and the miles per gallon compared to the percentage decrease in price. For my correlation coefficient I will be using the age compared to the percentage decrease in price

Prediction

I predict that the mileage will affect the price of second hand car the most. This is because the mileage determines how much the car has been used and this leads to the quality of the engine. I also think that the number of owners and the age will affect the prices of second hand cars tremendously.

Middle

3

37.5

29000

1

5

11800

4700

7100

60.17

5

37.5

34000

1

9

170841

37995

132846

77.76

8

13

55000

1

20

7864

4500

3364

42.78

3

44.5

13000

1

3

39728

6250

33478

84.27

7

Unknown

Unknown

2

18

8680

3200

5480

63.13

4

57.5

27000

1

7

12590

4300

8290

65.85

4

38

17000

1

5

24086

2975

21111

87.65

5

33

96000

1

14

17795

3400

14395

80.89

5

33

66000

2

11

10954

6795

4159

37.97

1

41

3000

1

5

13975

5795

8180

58.53

3

32.5

53000

1

10

16139

6995

9144

56.66

5

37

35000

2

16

14505

8800

5705

39.33

2

41

7200

1

6

13230

8250

4980

37.64

3

37.5

34000

1

9

9125

7500

1625

17.81

1

52.5

18000

1

4

Results

The age and Percentage decrease (correlation coefficient)

To work out the correlation coefficient I used the formula that I provided before and the results that I got resulted in this graph. I worked out the correlation coefficient the number that I ended up with was 0.7692875 this is relatively high and this shows that the line of best fit is very relative to the points that have been plotted. If the correlation coefficient were 1 then it would be a perfect positive correlation if it were –1 then it would be a perfect negative correlation. Evidently this means that if the correlation coefficient were 0 it would have no correlation. But overall this graph shows that the older the car gets the bigger the percentage decrease gets i.e.

Conclusion

I predicted that the factor that would affect the price of second handcars the most would most probably be the mileage, I was not that wrong but in as wrong in saying that the mileage is the biggest factor because I found out by the use of the correlation coefficient that the age is the biggest factor. After the age it was the mileage and then the insurance group. This was a surprise to me as I thought that the number of owners would have had a bigger affect but evidently not as much as the others, I find this hard to comment on because I feel that I have chosen the wrong graph do demonstrate the point that I wanted to show, I should have used a histogram or maybe a frequency polygon. I feel that the results are slightly bias as all of the results that I chose where chosen as I thought that they would all affect the price of second hand cars rather than choosing a random selection of topics for discussion, this would be a way of eliminating the bias. I also think that if I had chosen a bigger sample size I would have achieved more accurate results, as the correlation coefficient will probably become more accurate and may have changed the results of my whole project.

Jaysal Bodhani 10 MMB

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics section.

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