Method

My first part of investigating was to look at the data sheet. Then think of the way I would do the sampling I had three different ways in which I could have done my sampling in stratified, which is a sample in which the population randomly sampled. Anther one is random sampling which is choosing sample at random Lastly, selective sampling which is a selective sample is one which every nth term is chosen where n is chosen at random. I considered all three, I chose selective sampling because it s the easiest, and would see a pattern more easily, I chose the every fourth car on the data sheet and looking at the age and mileage price. Than I put all my selective data in a table and found out the percent changes. Than produce a scatter graph on graph paper and used the graph to do the line of best fit. Next step was to get my cumulative frequency, I done this by get all the 1 years old 3 years 5 years 7years 9 years olds from the data sheet put them in tables using the results to make box whisker plots, then analyse the results.

Conclusion

I scatter graph shows line of best fit. The first thing I noticed about the graph was that it was a positive correlation between the price and the mileage. The spread of Data looked very uneven so it was hard to tell. Positive meaning higher age/mileage, the higher percent change which shows us how much the age and mileage effect the prices on the cars. From the graphs, I can now predict future cautions by using the line on best fit as show on the graphs. For example, a 4-year-old car looses 50 percent of its value; also 2 year old looses 35.5 percent of value. The box whisker plots show as the age increase the median percent also increase so the actual year has an effect on the value of the cars.

I have found out that age effects the prices the most, mileage also effects the price not as much as the age does. Going back to my hypothesis, which proves that my Hypothesis was correct, because I said that age will affect price more than mileage which I did. I had some difficulties doing this the time restriction, missing data because we only used 100 cars we could have got more from the autotrader exchange mart. I could have taken it further by comparing the models and makes, looking at other factors such as the engine size, condition, type and the model of the car. I think these other factors together with the things like mileage age and make all together contribute to the price of the car.