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• Level: GCSE
• Subject: Maths
• Word count: 1521

# Car sales

Extracts from this document...

Introduction

Ben Mister         11N        Car Sales

Car Sales Data Handling Project

Introduction

This is a project on sampling fifty cars from a database of over two hundred cars.         The sampling was done by random selection.

I am investigating the comparative relationships that the age and mileage of a car have on the used price.  I will compare the used price of 50 cars, which have been randomly selected, to their age/mileage.

In order to do this investigation I am using an Excel Spreadsheet that contains data on cars of differing ages and mileage along with other details listed in the screen below-

Hypotheses: The older the car, and the higher the mileage of the car, the lower the used price will be.

Plan

The data could also be analysed in many ways

• I will obtain the information from a data base in the program excel named, ‘Car Sales’.
• I will investigate the relationship through graphs and charts- created in excel
• The fixed variable that I have chosen is the used price of the cars.
• I will investigate what factors affect it.
• I will be investigating a range of variables to go with used price. These include
1. price new
2. price used
3. age
4. colour
5. engine
6. fuel
7. MPG
8. mileage
9. service
10. owners
11. length of MOT
13. insurance
14. doors
15. style
16. C looking
17. seats
18. gears
19. Air con.
20. air bags

I will not be investigating every variable, but

Middle

Pie charts

I have made pie charts for both age and make.

Age

This shows that most cars are in the range of 5-9 years old.

Make

This shows that most people drive fords.

Scatter Graphs

In the first graph comparing the used price and the age of the car you can see that there is a clear negative correlation, as predicted.

These are the results of my second graph:

As you can see there is no obvious correlation between the used price and mileage. (This doesn’t go with the prediction.) I think to resolve this problem I should re-do this graph using the mileage and the percentage change in price. I did this and the results are shown below:

Anomalies-

As you can see this now gives a positive correlation, this still is not how I predicted the outcome but at least it shows a better result than the previous graph. But as you can see, some cars actually increased in price this gives a large anomaly. This anomaly could be caused by a car that instead of depreciating in value actually increases. When I went to remove this anomie I noticed that it was a classic/vintage car.

.

Below is the same graph without this anomaly. I did this buy removing the outliers which were indicating an increase in price.

Means & medians

Conclusion

This shows that there is a strong negative correlation between the used price of the Fords, and their age of them. This means that, as the car gets older, the car deprecates in value. Fords depreciate in value slower than Mercedes.

Unlike the Mercedes data the correlation here is strong as the R2 amount is above 0.5, which means it is a strong correlation.

Conclusion

My results show that as cars get older, they deprecate in value. Although there are a few anomalies, such as vintage cars that increase in value. The rule generally applies to all car makes and models.

The R2 amount for the Ford data is higher than the amount for Mercedes; this means that Ford has the strongest correlation between the two.

Overall my prediction was correct, as I predicted ‘I predict that the older the car, and the higher the mileage of the car, the lower the used price will be.’

,my scatter graphs show this.

## Evaluation:

If I was to redo my investigation I would have changed a few things, firstly I would have used a much larger database of information, this is because, although I collected some results that were acceptable, my results would be much more reliable if I had a larger amount of information to work with. And then my results might show stronger trends and correlations.

Also I would probably select different variables to instigate, I would try and choose variables that would show different correlations so that I could make better comparisons.

26/02/2008

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

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