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  • Level: GCSE
  • Subject: Maths
  • Word count: 3272


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For my maths/statistics coursework I am to analyse a set of data given to me. The purpose of this investigation is to distinguish any possible correlation between certain variables. Furthermore, I must determine what factors affect the price of a car. From my data I am looking at particular variables as I feel these variables will the most subsequent to the price. The variables I am going to investigate are as follows:

  • The age of the cars.
  • The mileage of cars.
  • The engine size.
  • The makes.
  • The cost of the car when new
  • The second price.

Initially, I am going to find any obvious links between the above variables. Moreover after finding the link’s I will develop the data in an attempt to locate the influential links that affect the price of the car. To assist me in doing this I am going to draw graphs and obtain conclusions that will lead me on to finding a general formula.

Out of the data given to me I have used the random function on my calculator to generate random number with which I was able to collect the cars I hope to analyse. Out of 100 cars I have chosen 36. I feel that 36 are a sufficient number to get the best possible conclusion.

...read more.












  1. Out of all the variables I have chosen to investigate I think that the age and mileage will have the best correlation. I believe that as the age goes up so will the mileage.
  2.  Furthermore, I feel that as the mileage increases the price of the car will decrease. The more the mileage the less the price.
  3. I believe out of all the variables engine size will not provide me with a strong conclusion. In contrast I feel that the make will decide the price of a car to a certain extent.


Firstly, I am going to plot graphs variables I have chosen to investigate. I will also use standard deviation and rank correlation coefficient to give me an idea if the correlation between certain aspects of my data. I will also use other statistical methods such as mean median quartiles box plots etc.


To begin with, I am going to evaluate the price of all 36 cars with their age and mileage. Using my own knowledge I speculate that age and mileage should have an obviously correlation, thus making the price of the car cheaper.  



From the above graph it is clearly noticeable that as the car gets older price goes down. Thus representing the fact that a new car depreciates at a faster rate than an older car. For example, a new car that is about a year old must depreciate at an excessive rate than that of a 10-year-old car. Furthermore, suggesting that as the age goes up the price depreciates less.

...read more.


image02.png. If you plot a graph for %depreciation and age and substitute in the values you should end up with a average formula that should fit all brands. I felt that I needed to focus more on this part of the project that would enable me to come up with a formula that could fit certain or all brands. I think that there were sum mistakes in the data that led to some unexpected correlation. In conclusion I felt that the most contributing factors were age and mileage because they played a huge part in my data and showed a close relationship. As the mileage increased so did the age.

...read more.

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