Firstly I shall find and group the most admired car in my data:
Now that I have found and grouped the most popular cars I will now have to illustrate a pie chart to show, which make is truly the most popular:
By looking at the pie chart and my tally chart I can determine that out of my data the most popular car is Ford.
Now that I have looked at the most popular make I will now find and group the most popular colour:
Now that I have grouped the data I can now put the information in a pie chart:
By looking at the pie chart we can see that out of the data the colour red is the most popular.
Now that I have grouped the data I can now see that the most popular:
Make = Ford
Colour = Red.
I have done and finished grouping my data now I shall use the technique of random sampling to choose the cars I am going to investigate and evaluate. The way I am going to do this is by using the nth number of car. My number is five consequently starting from the first car I will highlight the every fifth car because these will be my samples. I am also doing this because I am avoiding being biased by using this method therefore giving every make an equal chance.
The cars that were highlighted and preferred for the investigation are:
5 = Nissan Micra
10 = Vauxhall Tigra
15 = Fiat Punto
20 = Nissan Micra
25 = Rover Metro
30 = Volkswagen Golf
35 = Ford Fiesta
40 = Fiat Tipo
45 = Ford Escort Duet
50 = Daewoo Lanos
55 = Fiat Punto
60 = Rover 620Si
65 = Ford Puma
70 = Fiat Bravo
75 = Rover 623 GSi
80 = Ford Escort
85 = Vauxhall Corsa
90 = Peugeot 306
95 = Rolls Royce Silver Spirit
100 = Vauxhall Vectra
Firstly I will find the price depreciation of each of cars.
By looking at the table I can now look at the percentage depreciation of each car to see which car out of my sampled cars has lost the most value. The car that lost the most value was the Ford Puma it decreased by 62.36%.
Now that I have found the samples I am going to use I am going to find the 2nd price of these cars.
5. £3999
10. £7499
15. £3995
20. £1759
25. £859
30. £3695
35. £1495
40. £1500
45. £2300
50. £4395
55. £4500
60. £3400
65. £8250
70. £4995
75. £2975
80. £3495
85. £3495
90. £3995
95. £14735
100. £4995
Next I shall find the age of the cars:
5.3
10.4
15.4
20.8
25.7
30.7
35.11
40.7
45.7
50.3
55.3
60.5
65.3
70.2
75.6
80.7
85.6
90.6
95.9
100.5
My scatter graph has a good negative correlation meaning that as the age increases the 2nd hand price decreases. Next I will find the equation of the line of best fit. The equation for the best fit line from y on x is y =- 0.91x+8.9. The r stands for how far are the values from the mean on both x and y axis set of numbers. It is called the correlation co-efficient. The statistics box shows the mean of the 2nd hand cars (x axis) and also the mean of the age of the cars (y axis). It also shows the range of both the 2nd hand cars and the age of the cars. The standard deviation is the deviation of the mean. It’s the spread of data of the mean it also uses all of the data in the graph.
Therefore the standard deviation of this graph is 1.85(x axis) and 2.26(y axis).
Now that I have done the line of best fit I will make predictions from the line to show what the approximate price would be for a different age or the other way round.
1st prediction:
I chose the age of 5 I will now go across on the graph to check the price. After I have reached the line of best fit I go down and see what the predicted price for a car aged 5 is. Therefore, the approximate price for a car aged five is about £4000.
2nd prediction:
I chose the number 6; as a result the predicted price for the car is approximately £3100.
3rd prediction:
Now I will choose a price from the x-axis and see what the predicted age would be. Therefore I chose the price of £1000 the predicted age of the car is 9 years old.
As you can see as the price decreases the age increases meaning the older the car is the less the consumer has to pay.
Now that I have investigated and evaluated the factor of age on the pricing of a second hand car I will now investigate the factor of the engine to see whether the Engine “plays a part” in pricing a second hand car.
The engines of the cars chosen are:
5. 1
10. 1.4
15. 1.2
20. 1.2
25. 1.1
30. 1.4
35. 1.8
40. 1.4
45. 1.4
50. 1.4
55. 1.2
60. 2
65. 1.4
70. 1.4
75. 2.3
80. 1.8
85. 1.2
90. 1.9
95. 6.7
100. 1.8
Now that I have stated the mileage
I will now put this information in a scatter graph to see whether it has an effect on the pricing of a second hand car:
By looking at the graph I can determine that it has a positive correlation. This means as the engine size increases so does the price of the second hand car. The equation for the line of best fit is y=0.3x+0.46. The mean engine size is 1.75 and the range of the engine is 5.7. From this data we can now make predictions about how the price affects the engine. Also the standard deviation is 2.99 (x axis) and 1.18 (y axis).
1st prediction:
I chose the price of £6000 therefore the predicted engine size would be 2.4.
2nd prediction:
I chose the price of £2500 thus the predicted engine size would be 1.2.
3rd prediction:
I chose the engine size of 1.5 for that reason the price would be £1000.
4th prediction:
I chose the engine size of 4.0 therefore the price would be £10 000.
From this I can infer that as the engine size increases so does the price of the 2nd hand car and also as the price increases the engine size also increases therefore showing that the engine is a factor of pricing the 2nd hand cars.
Now that I have investigated the engine of the cars I will now investigate the mileage and whether it is taken into consider when pricing a 2nd hand car.
The mileages of the cars chosen are:
5. 37000
10. 27000
15. 31000
20. 47000
25. 43000
30. 49000
35. 74000
40. 32000
45. 64000
50. 32400
55. 13000
60. 66000
65. 34000
70. 18500
75. 96000
80. 43000
85. 55000
90. 71000
95. 70000
100. 52000
Now that I have the mileages I can now put the data in a scatter graph:
By looking at my scatter graph I can determine that it has weak negative correlation. This means that as the mileage increases on a car the price decreases. This means the higher the mileage on the car the lower the price is. The equation for the line of best fit is y=-0.49x+6.54. The mean mileage of cars is 47700 and the range of the mileages is 83000. With this data I can now make predictions of what the mileage of a car would be at a certain price or the other way around.
1st prediction:
I chose the price of £7000 consequently the approximate mileage of the car will be 10000.
2nd prediction:
I chose the price of £5000 so the predicted mileage would be 22000.
3rd prediction:
I chose the mileage of 30000 hence the price of the car would be £4200.
4th prediction:
I chose the mileage of 65000 as a result the predicted price of the car would be £2000.
From these predictions I have found that the higher the mileage the lower the price because of the factor that the car has travelled more. Therefore showing that mileage does “play a part in pricing” the car.
Now that I have found and investigated the three factors concerning the pricing of a 2nd hand car. Now I will choose the two most popular car makes and compare them with their prices. So, the two most common makes in my data are Ford and Vauxhall. Now I am going to take all the cars from each make and group them.
Firstly for the Ford cars in my data:
Now for the Vauxhall cars in my data:
Firstly I will do a cumulative frequency table and graph for the Ford makes using the 2nd hand prices:
As you can see on the graph I have an s-type curve which means the graph is o.k. I have also found the upper quartile median and upper quartile. With this I can draw and come up with a single box plot:
Now that I have done I shall do the same with the Vauxhall cars:
The inter-quartile range for the Ford makes is: 9.
The inter-quartile range for the Vauxhall makes is: 9. Therefore for both makes the inter-quartile range is the same. Now I shall put both of the box plots in one diagram to compare the prices for the makes:
The box plot I have produced shows that because the Vauxhall box is shifted more to the right the 2nd hand cars have generally higher prices than Ford. Also because the median line is more to the upper quartile in the Vauxhall box this means also that Vauxhall 2nd cars are normally expensive. Whereas in the Ford box the median is more to the left meaning that Ford 2nd handcars are usually cheaper.
To conclude this investigation, second hand cars are priced with varied factors. In this investigation I evaluated 3 of those factors (mileage, engine size, age) and all of these factors had an influence on pricing a 2nd handcar. One of these factors proved my hypothesis correct by showing when the age increases so does the price. Also as the mileage increases the price decreases and lastly as the engine size increases so does the price. I have also compared the two makes (Ford, Vauxhall) which showed me that Vauxhall 2nd hand cars are most commonly more high-priced than Ford 2nd hand cars. In conclusion my investigation shows the dissimilar affects of different factors and how one singular make compares with other makes, in general the investigation was a success!
