Objective; to investigate the relationship between used car price and age of car.
30/6/03 Mathematics Coursework C+H/WK
Objective; to investigate the relationship between used car price and age of car.
Equipment; to do this task I will need the following equipment;
Ruler,
Pencil,
Pen,
Multimedia computer (to write up investigation in neat),
And plenty of A4 paper.
My Hypothesis;
Since all the cars from the given data base are MODERN cars, I predict that they ONLY decrease in value once they are bought. It would be different for really old CLASSICS as they can be so old that they actually increase in their value as they age. This is because;
. The demand of these cars rises as few are left available, from being scraped.
2. Since theses cars lack modern technology of galvanised metal, they suffer from rust, thus people selling them in mint condition spent thousands of pounds restoring the car to it's original condition, hence they want that money back when they come to sell it.
3. The cars are different from modern cars, and give the owner a high status through being so immensely unique.
Because all these cars in the database are all modern, the cars should, in my prediction decrease in value as they age and those that are guaranteed to become classics may depreciate slower in value than those that aren't. Also those of luxury and prestigious names like Mercedes, Bentley and Rolls-Royce, and those that have a high demand will loose their value more slowly then those that aren't, but just as a generalisation, no matter what car i choose, the older it is, the lesser it's value is against those that are younger. This means that in the graphs I produce there is a TREND in the cars correlation which must be NEGATIVE in order for my hypothesis to be true.
Sampling; (Planning)
My plan involves selecting 40 cars from the total of 100 cars by car in the list. To do this I used stratified sampling which involved taking a tally of all the cars arranging them in sections depending on their age, then as 40/100 is 0.4 I multiplied the frequencies for each age group by 0.4 and rounded it to the nearest integer where the total would add up to 40 cars. I decided to leave out the cars of ages 11+ since their was only a maximum of one car for each year ,hence when I multiplied by 0.4 and rounded to nearest integer I got 0. Also because there were no cars of ages 12,13 and 14 years this would leave a massive gap in between 11 and 15 years, leaving under sufficient data in the graph to be properly analysed and a proper, proved conclusion to be made. I also did this because if I just picked out cars randomly like every 3rd car in the list, it wouldn't prove to be a fair test, as if I did, I may have neglected cars of certain ages out, and/or got too many cars of the same age group. Therefore, again incorporating this data onto a graph would leave massive gaps and the points would be too sparse in between to analyse properly and make a conclusion to the objective that is guaranteed true- with clear evidence from data and positive backup from my predictions of prices on other real cars from the remaining database.
The stratified frequency table overpage informs you of the list of cars of 1-10 years old, and the amount of cars that I will pick out after multiplying by 0.4 and rounding to it's nearest integer, so altogether the total will add up to 40 and my graph will be adequate enough to be fair, hence my conclusion confirmed fully true, my hypothesis confirmed either true or perhaps untrue, all from fair evidence in my investigation procedures and results.
For each number of cars I have to pick out I will choose each 3rd car from the database of that certain age group an take note of it's name and obviously it's used price. After I have acquired all this relevant information I will find the mean value for each car age group hence another graph as well as the first one(with all 40 cars pitched on it)that gives the mean value for each year. This is easier to follow since there are only ten points in comparison to the previous 40, therefore clearer for me to back up my conclusion to the task at the end, hence being is a better reference to see whether or not there is a strong trend between the data, as every cars values are equally blended in to the graph, and all equally count with no car, whether it was out of the trend in the first graph or not, neglected from it.
Stratified Frequency table to show how may cars from each age group are to be picked
Age of car in years
Frequency
40% of cars available
No. of cars to be picked
0
4
4
2
9
3.6
4
3
8
3.2
3
4
2
4.8
5
5
8
3.2
3
6
8
7.2
7
7
4
5.6
6
8
0
4
4
9
5
2
2
0
3
.2
2
Total = 97 cars
Total = 40 cars
As you can clearly see that the bottom one should have been rounded to 1 but I instead brought it up to 2. This is because 1 car alone is unsatisfactory, considering that the mean amount of cars picked is 4 and that 1 car hasn't enough range or even any of range prices, thus I'd get a more accurate measurement of the prices of 10 year old cars if another was added, making my conclusion even stronger for evidence and backup. And in the second graph I'd also get a mean price for ten year old cars between the two I'd picked, whereas with one, the mean, median and mode are all the same, reducing the test being fair and accurate to unacceptable measures.
Then, for picking each car from the data base for each respective age group, I decided that stating the name and type of the car is actually necessary, despite the fact that I am looking at the relationship of car age to used value and not car name/make to used value. This is because of if in the case of weird occurrences where one or more cars are vastly out of the other car values'trend',or even if there happens to be no trend at all! This is because knowing what the car is, should make it easier to explain why it doesn't follow with the other cars data's correlation. Obviously, after all is settled in this part of the task the next thing I would do is to incorporate the data into a scatter diagram which should interpret whether in general the prices go up or down depending on the type and how much correlation(If any) the graph shows.
On each side of the graph should be, the age of the car(Going across the x axis ) and the age of the car going up along the y axis. This should be enough information, and proof for me to write my conclusion to the objective, making proof of whether my hypothesis is correct, why and what the relationship is, hence fairly and honestly evaluating and the task.
Below is the list of named cars that I picked from the database for each age groupie and the value of it in pounds(£)
Age 1 ...
This is a preview of the whole essay
On each side of the graph should be, the age of the car(Going across the x axis ) and the age of the car going up along the y axis. This should be enough information, and proof for me to write my conclusion to the objective, making proof of whether my hypothesis is correct, why and what the relationship is, hence fairly and honestly evaluating and the task.
Below is the list of named cars that I picked from the database for each age groupie and the value of it in pounds(£)
Age 1 year(4 cars);
Fiat bravo-6795
Honda Civic-7995
Volkswagen Beetle-13,500
Mercedes A140 Classic-10.999
Age 2 years(4 cars);
Vauxhall Corsa-4,995
Fiat Bravo-4,995
Vauxhall Vectra-7,999
Mitsubishi Carisma-5,999 (continued overpage)
Age 3 years(3 cars);
Daewoo Lanos-5,999
Fiat Punto-4,500
Nissan Micra-3,999
Age 4 Years(5 cars);
Rover 623 Gsi-6,999
Fiat Punto- 3995
Nissan Almera-4,300
Fiat Punto-3,769
Daewoo Nubria-6895
Age 5 Years(3 cars);
Rover 620Si-3,400
Volkswagen Polo-4,693
Fiat Bravo-3,495
Age 6 Years(7 cars);
Rover 416I-3,685
Vauxhall Calibra-6,995
Ford Escort-2,995
Rover 416I-3,795
BMW 316I-6,995
Hyundai Accent-2,800
Rover 623Gsi-2,975
Age 7 years(6 cars);
Volkswagen Golf-3,695
Fiat Tipo-1,500
Nissan Sunny-2,595
Ford Escort-3,495
Ford Escort-1,595
Seat Ibiza-795!
Age 8 years(4 cars);
Fiat Uno-1,495
Renault Clio-1,995
Citroen Xantia-2,450
Nissan Micra-1,795
Age 9 years(2 cars);
Nissan Micra-1,595
Hyundai Sonnata-1,195
Age 10 years(2 cars);
Ford Fiesta-1,664
Vauxhall Nova-1,000
The cars in bold are cars that were picked by me without using the every third car sequence, but because I landed on the same cars I'd already picked. Thus the fairest thing I thought I'd do was to pick the very next car of that age group instead. This is because if I choose a car that I new would backup my hypothesis and perhaps had the same price as the other cars of that age group the investigation wouldn't necessarily be fair, since the cars I'm picking should be random, not of my own choice and benefit.
Picking the next car straight after is the simplest most hassle and time saving way of picking another car randomly. I decided to pick every third car from the database for each age group because picking the first cars I come too isn't taking cars from all around the data base as I've probably picked all the cars I need before I've got through the first 20 cars in the list. Same applies with picking every second car for each specific age group, hence I decided picking every third car was the best way to retrieve the car data.
Processing the data into scatter diagrams
I drew this on normal paper to prove that I'm not cheating and taking short cuts on Microsoft excel to process graphs because this is a practice course work task and I need to know exactly what I should use to present my graph work to gain the most marks when it comes to the real thing.
Hence I decided to use both resources just incase human-error(My error!) happened to cause significant changes in the appearance of any existing relationships between the car value and age, thus again putting error into my conclusion. By using Excel I can produce graphs I know I can rely on as true, fully accurate information as computers never make mistakes now do they???...Or at least mine doesn't anyway.
In order to do this I first of all arranged the data inevitably in order of age as this table below shows with each cars price on the right, and it's age on the left.This was done in a list in individual cells in Excel. Below is what the data looked like before it was fully processes by me and the computer into a graph(over page).
Age of car in years
Price of car in pounds(£)
6,795
7,995
3,500
0,999
2
4,995
2
4,995
2
7,999
2
5,999
3
5,999
3
4,500
3
3,999
4
6,999
4
3,995
4
4,300
4
3,769
4
6,895
5
3,400
5
4,693
5
3,495
6
3,685
6
6,995
6
2,995
6
3,795
6
6,995
6
2,800
6
2,975
7
3,695
7
,500
7
2,595
Rest of Excel table data over page
The rest of the Excel table data that was processed into the first scatter diagram to be analysed;
Age of car in years
Price of car in Pounds(£)
7
3,495
7
,595
7
795
8
,495
8
,995
8
2,450
8
,795
9
,595
9
,195
0
,664
0
,000
From this data I manage to process this Scatter diagram below on excel and my own scatter diagram I
drew my self overpage.
As you can clearly see from both of my graphs that NEGATIVE correlation is present- the cars prices do fall as they get older. My line of best fit clearly goes down to prove this statement is correct.
Also which is blantly visible is that the correlation is weak-the cars of age 1 year have the largest range in values with the cheapest at approx.£7,000 and the most expensive at almost double the price at £13,500. This is because the cars have a different spec and status, hence their price differing dramatically when they are still young-The most costly car is the Volkswagen Beetle, because it is a new unique car built by quality manufactures with excellent, build, engines and equipment. It was built as a replacement to the old beetle, as an expensive fashion accessory for people who care about how they look and want to look flash. Since the other cars in this year aren't as flash and unique as the beetle they are cheaper, hence the fact that the beetle is out of the trend. On the other hand for the year old cars, the cheapest of the bunch is the Fiat Bravo-old fashioned, badly built, sparsely equipped, and with no image, it's quite obvious why this car is some much cheaper than the others, thus so much out the trend. Mostly from two years onwards to 6 years the cars values fall steadily, at a constant rate with very little range between the most expensive and the cheapest cars values. Until I noticed at about 6 and 7 years most of the cars values drop dramatically, with the most expensive car value for 6 years at approx£7,000 and the most expensive car value for 7 years at less than £4,000. Also the cheapest car for 6 years is approximately £3,000 against the cheapest for 7 years at approximately £750! This is partly because cars were only built to last for 6 years, with anti perforation warranties expiring after 6 years had passed. Thus the value of the car drops dramatically, since if anything may go wrong the owner cant get it fixed for free by the manufacture, they have to do it themselves, or pay someone else to do it. It's also partly because again, the cars that are the most costly are prestigious models that depreciate slower than others, which aren't included in the 7 year old age group-only the 6 year old group. The car that's the most expensive and way out the trend in the 6 years old age group is Vauxhall Calibra- a hot super car that's well equipped, spacious, prestigious and refined. It's also well built to and the fact that it has a turbo keeps it's price is high in the sky too. In comparison to the other cars that are 6 years old(Ford's, Rovers, Hyundai's and small compact BMW's) It's easily noticeable why this car is way out the other cars trend of values. For those cars of 7 years, most of the cars fall a a steady rate in value relating to most the other cars trends that are both older and younger, except one-the car at approx.£750-The Seat Ibiza. This car is cheap because;
. it's been replaced by a better model
2. It's a cheap clone of Volkswagens and Fiats.
3. It's poorly built, sparsely equipped, slow, noisy, unrefined, unreliable and unsafe.
4. There are lots of them about, thus there is low demand for them and they're hard to sell.
All the other cars in the 7 year old age group are much better, made by much better reliable names, and are bigger and better equipped.
Also I found a false priced car in the 10 year old age group- the £1,664 Ford Fiesta. As you can see from the line i drew on the graphs the 2 other cars I mentioned that were vastly higher in price than the others for the same age join coincidentally almost perfectly up together with the fiesta at the bottom. I know it is false as the Ford Fiesta is a very simple car that owners wouldn't sell for a complex price- probably £1,700 not an absurd £1,664!.
However as the rest of the data presents the older the car is the lesser it's price is against those that are younger. This means that my Hypothesis is correct but just to be sure before I do my predictions I will produce another scatter diagram which gives the mean price data for each car age group. This means adding all the cars prices together for each age group and dividing it by how many cars there are in that age group;
Mean price for 1 year in pounds(£) = (6,795+7,995+13,500+10,999)/4 = 32,289/4 = 9,822.25
Mean price for 2 years in pounds(£) = (4,995+4,995+7,999+5,999)/4 = 23,988/4 = 5,997
Mean price for 3 years in pounds(£) = (5,999+4,500+3,999)/3 = 14,498/3 = 4,832.67
Mean price for 4 years in pounds(£) = (6,999+3,995+4,300+3,769+6,895)/5 = 25,958/5 =5,191.60
Mean price for 5 years in pounds(£) = (3,400+4,693+3,495)/3 = 11,588/3 = 3,862.67
Mean price for 6 years in pounds(£) = (3,685+6,995+2,995+3,795+6,995+2,800+2,975)/7
=> mean price = 30,240/7 = 4,320
Mean price for 7 years in pounds(£) = (3,695+1,500+2,595+3,495+1,595+795)/6 = 13,675/6
=> mean price = 2,279.17
Mean price for 8 years in pounds(£) = (1,495+1,995+2,450+1,795)/4 = 7,735/4 = 1,933.75
Mean price for 9 years in pounds(£) = (1,595+1,195)/2 = 2,790/2 = 1,395
Mean price for 10 years in pounds(£) = (1,664+1,000)/2 = 2,664/2 = 1,332
Then I used exactly the same procedures as with the last graph, listing the car data on excel to process it onto a scatter diagram over the next page. This should show full proof or disproof of my hypothesis and allow me to make prediction on car values in comparison to their ages and vice versa for me to write up my conclusion.
This is an exact copy of the table of data I used on excel, this time to produce my second scatter diagram, which shows the relationship between the mean of the used car price to its age:
Table of data used to process scatter diagram showing relationship between MEAN car price and age
Age of car(s) in years
Mean price of car(s) in pounds(£)
9,822.25
2
5,997
3
4,832.67
4
5,191.60
5
3,862.67
6
4,320
7
2,279.17
8
,933.75
9
,395
0
,332
From this data I managed to process this Scatter diagram below on excel and again my own scatter diagram I drew myself overpage.
I produced another graph with the same data as the other on the other page, but with a smooth curve joining each dot so I could make predictions on cars of ages not exactly ages 1 to 10 years(eg; 4 years 7 months.) This implies that the graph on the other side3 is enough proof to back up my hypothesis-that the cars reduce in price as they get older. However as this smoove curved graph below suggests there are variences in price as between certain ages the prices reduce at different rates and may slightly rise in some cases. But because this is taken from data of 40 cars not the whole worlds the fact that between the following ages and conclusions that I am going to make aren't fully justified-and never will be since inflation rises everyday causion what we thought 10k was a lot into as little as 10p!(it's not that dramatic but it makes my point!)Also because I will never be able to that data from every single car in te world between ages one to ten in less than a year-as I said before my hypothesis is just a GENERALISATION from the database I was given, thus all the points I make in my conclusion are too.
Below is the graph with the smooth curve I will us to predict and compare values of REAL cars with:
Predicting and Confirming
From this graph I predict that any 4 year old car I pick it will be:
. Around £4,500- £5,000.
2. It will be +/- 5% off 50% it's value when it was 1 years old,
3. It will cost between £750 and£1,000 more than if it were 5 years old,
4. It will cost roughly twice as much than if it were 7 years old.
All the cars I used in my predictions were picked from an ultra-reliable, accurate and honest car pricing website-WWW.Parkers.co.uk;
I randomly picked each car fairly by numbering each of the Manufactures in the list below from one to six and what ever it landed on would then be numbered in order from one to six until eventually I was down to 1 manufacture. The type of car and actual car that I picked from that manufacture was done in the same process.
Below is the list of Manufactures that I numbered 1 to 6 simultaniouly to be ramdomly, hence fairly selected
. Alfa Romeo
2. Audi
3. Austin
4. BMW
5. Caddilac
6. Chevrolet
. Chrysler
2. Citroen
3. Dacia
4. Daewoo
5. Daihatsu
6. Daimler
. Ferrari
2. Fiat
3. Ford
4. Honda
5. Hyundai
6. Isuzu
. Jaguar
2. Jeep
3. Kia
4. Lada
5. Lancia
6. Landrover
. Lexus
2. Lotus
3. Mazda
4. MCC Smart
5. Mercedes-Benz
6. MG
. Mini
2. Mitsubishi
3. Nissan
4. Perodua
5. Peugeot
6. Porsche
. Proton
2. Renault
3. Rover
4. Saab
5. Seat
6. Skoda
. Ssangyong
2. Subaru
3. Suzuki
4. Toyota
5. Vauxhall
6. Volkswagen
. Volvo
I shook the dice and it landed on 5, hence those cars manufactures listed below went through to the second part of being selected, thus they had to be renumbered;
. Caddilac
2. Daihatsu
3. Hyundai
4. Lancia
5. Mercedes-Benz
6. Peugeot
. Seat
2. Vauxhall
I shook the dice a second time and it landed on 3, therefore a hyundai car had to be randomly chosen by again numbering each model from 1 to 6 and the dice being shaken for the third time;
. Accent
2. Atoz
3. Coupe
4. Lantra
5. Sonata
6. XG30
I shook the dice for the third and last time and it landed on a 4, thus the car I'd chosen to compare against my interpretations of the data I'd processed was a 4 year old Hyundai Lantra. Below is how that cars data compares with my predictions relating to the processed data;
I said it would be approximately 45%- 55% it's value as 1 years old-I was correct as it's value at 1 years is £9,340 and it's value at 4 years is £4,615, and (4,615/9,340)*100 = 49.41%which is near enough 50%.
Also from the data I suggested that the cars value would be around £4,500 and £5,000 and £4,615 is in between those prices thus that part is also correct too.
I assumed that the cars value would be approximately twice it's value that when it is 7 years old; this is also correct as the exact same car at 7 years old costs £2,410 and this is almost half of £4,615 as (4,615/2,410)*100 = 191.49% which is almost 200% the value that it will be at 7 years compared to 4 years old.
I also reckoned that it would cost around £750-£1,000 more at 4 years old than if it were 5 years old. This is wrong as the exact same car at 5 years old is £4,045 and the difference between it's value at 4 years and 5 years is 4,615-4,045 =570 which is almost £200 less than the minimum difference I expected it to have.
But as all that predicting went the conclusions I made were 75% accurate with 3 correct and 1 wrong.
But for this to be a fair test I will have to test 2 other cars an over all the accuracy of my predictions relating to the interpretations on the graphs must be above 50%.
So for the second car it was exactly the same process as last time to randomly and fairly select a car from the list of manufactures by rolling the dice several times. The car this time would be 7 years and 6 months old;
I predict from the graphs data that any car that gets picked will;
. Be around £2,000 or 25% it's value when it was 1 years old.
2. Be half it's value when it was 5 years old.
3. Be around 50% more than the value it would be at 9 years old
4. Cost around £1,000 £1,500 less than if it were 6 years old.
For my first roll it landed on a 1, thus the list of car manufactures below were to be put through to the second throw;
. Alfa Romeo
2. Chrysler
3. Ferrari
4. Jaguar
5. Lexus
6. Mini
. Proton
2. Ssangyong
3. Volvo
I then rolled the dice again and it landed on a 3, hence on more round had to be done as both Ferrari and Volvo were numbered 3.
To be equally fair I let the odd numbers of the dice represent the Ferrari and of course the Volvo have the even numbers of the dice;
,3,and 5 = Ferrari
2,4,and 6 = Volvo
I rolled and landed on a 5, making the list of Ferraris below a possibility to be used against my predictions;
. 348
2. 456
3. F355
4. 348
5. 456
6. F355
I rolled the dice again for the very last time and it came up with a 3- the car to be tested against my predictions was a Ferrari F355 at 7 years and 6 months old.(continued over page)
The Ferrari according to parkers.com is worth £41,185!
This means that both parts to my first prediction are incorrect, as the car is worth around £40,000 not £2,000 and it's original value is £81,660 which means it is worth over 50% it's value after 7.5 years not around 25%!
My second prediction is also incorrect as the cars value is £49,320 at 5 years and half of that is approx £25,000 not £41,185!
The third prediction I made is also false as It's value at 9 years old is £36, 430 and that + 50% isn't any where near £41,185- it's nearer £55,000 at £54,645 instead!
Inevitably my last prediction was proved wrong too, as it's value at 6 years was £49,320 not between 1,000 and £1,500 more at approx £42,500 but £8,135 more- almost 6 times more!
For my last car it would be 10 years old;
I predict that it will be around 1/6 of it's value at 1 years old
I also predict that it will be between £750 and £1,500 in price
I predict that it will be around £1,000 less than it's value at 8 years old
And that it is between 20-25% it's value at 10 years old, compared to when it was 4 years old.
I rolled the dice and it came up with a 2, therefore the cars manufactures underneath were put through to the second throw;
. Audi
2. Citroen
3. Fiat
4. Jeep
5. Lotus
6. Mitsubishi
. Renault
2. Subaru
The second throw gave me a 3, hence A car from the fiat range listed below had to be picked from the decision of the third throw;
. Cinquecento
2. Punto
3. Cinquecento
4. Punto
5. Cinquecento
6. Punto
As you can see there are only 2 cars picked from a vast range of fiat cars. Why? Because all the others don't extend back as far as these do as in my predictions I needed a car the was 10 years old andstill had data of it's value at one year old. Only these two cars have this so I shaked the dice again for the last time with odd numbers for the Cinquecento and even for the Punt.
I rolled the dice and it came up with a 5, thus the Cinquecento was the car to compare with my predictions in relation to the graphs.
At 10 years the Cinquecento cost £820!
This means that my first prediction is correct as at 1 years old the car is worth £4,850 and
820/4,850 = 0.17(to2DP) which is roughly 1/6 or to be exact 82/485 which is close enough to 1/6.
This means that my second part is also correct as £820 is inbetween £750 and £1,500 which is what I predicted.
My third part is also correct as at 8 years old the same car is worth £1,765, and 1,765-820 = £945 which is very close to £1,000 which is what I predicted how much less the car would be at 10 years old in comparison to 8 years old.
However my forth prediction was incorrect as it's value at 4 years was £2,575, (820/2,575)*100 isn't between 20-25% but over 30% at 31.84%(to2DP)
As over all I have 6 out of 12 predictios correct this is enough satisfaction for me to write my conclusion, as even though this is the bare minimum for the conclusion to be confirmed, the Ferrari isn't really a modern car but a super car that litterally depreationless and cost 10 times more to buy than most other cars. Hence the fact it proved all my predictions wrong for a 7.5 years old car, where as the more modern cars like the Hyundai and the Fiat proved 75% successful. If it had been the Volvo instead of the Ferrari the I'm certain that with Volvos being cheaper modern, popular cars it might have related to some of my predictions bring up their success rate from 50% to a maximum of 83%(to2SF).
Concluding
So to conclude my points with clear proof from the graphs on pages 5-6 and 8-11 are;
That I am CERTAIN;
. The older a car gets the cheaper it gets.
2. Car values decrease most rapidly from when they are new to about 3 years of age.
3. Between 4 and 8 years car values decrease at a steady rate, about half the rate from 0 to 3 years old.
4. From 8 years onwards the cars value depreciates less and less but still continues to go down.
And from my successful predictions that;
. At 4 years a popular modern car is worth around half the value it was at 1 year old, and vice versa.
2. That at 4 years a popular modern car in good condition is worth between £4,500 and £5,000.
3. That at 4 years old it is worth double what it would be worth at 7 years old and that at 7 years old it is worth half what it is at 4 years old.
4. At 10 years old the car is worth around 1/6 than it is a 1 years old.
5. That Modern popular cars (like from the given data base I used to select the 40 cars with over page) of 10 years old are between £750 and £1,500 in price.
6. That most popular modern cars are around £1,000 less in value when they are 10 years old compared to when they are 8 years old.
And from my Unsuccessful predictions that;
. A cars value is around 30% it's value at 10 years old compared to when it is 4 years old
2. That super cars cost almost 20 times more than popular cars when they are 7.5 years old!
3. Than popular modern cars cost around £500-£750 more when they are 4 years old compared to when they are 5 years old.
4. That super cars have a difference in price between 6 years and 7.5 years 6 times more than then difference for the same aged modern, cheap and popular cars.
5. That the depreciation of modern cars by 7.5 years is twice as fast as that for same aged super cars.
Comparing these statements to my hypothesis I know that my hypothesis is correct as modern cars do depreciate in value, hence the reason why there is negative correlation in all the graphs, that more prestigious cars like the Ferrari do depreciate slower than those that are less desirable, and even the cheapest new cars like the new Volkswagen Beetle hold their value well because of their high demand. Also the part that says there's a trend in the cars values compared to their ages is true as from the first scatter diagrams only 3 out of about 40 cars were out of that trend.
Evaluating;
I would say that this investigation has been a success. I managed to find a link between the car price and car age and managed to make points on how this differs throughout certain ages and for certain types of cars.
If I were to redo this investigation, I would make sure that I had a bigger range of cars to choose from with a bigger age range of perhaps 1 to 50 years not 1 to 10. I'd also use cars in the graph that have ages between whole years like 4.5 years and 6.7 years etc...
I'd also used a larger sample of perhaps 200 cars instead of just 40 as which would give a better mean price for the value of cars in one particular age group.