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• Level: GCSE
• Subject: Maths
• Word count: 2089

# HEFP STATISTICS ASSIGNMENT REPORT

Extracts from this document...

Introduction

## HEFP STATISTICS ASSIGNMENT REPORT

Data Collection: We are asked to find the relationship between prices of  ‘51 registered’ cars and variables such as their current new price, mileage and weight by using the regression analysis. To collect full information of these registered cars, we can access to the websites like: http://www.exchangeandmart.co.ukHowever, in this easy, we use the data, which are provided with the assignment by our teachers.

By the sources, 100 cars, which manufacturers include Toyota, Honda, Nissan, Suzuki, are obtained to be sampled from. These Japanese cars can be divided into three groups according to different engine size, which are small engine size, medium engine size and large engine size. The cars’ engines size under or equal to 1.5 liters are considered as small engine cars. Group of medium engine cars are the cars’ engine size between 1.6 to 2.4 liters. Large engine cars are cars’ engine size equal or bigger than 2.5 liters.

The samples are likely to be representative because the method of simple random sampling will be used. The data of cars is wide range, so we use calculation to choose the samples randomly. The processes of sampling are divided into three procedures.

Middle

2

23000

£14,695

£17,008

147

28

Toyota

Pervia 2.4 CDX 5-dr

2.4

25963

£19,950

£22,995

154

29

Nissan

Terrano TD SE 5-dr 4*5

2.7

26572

£15,499

£21,595

123

30

Honda

NSX 3.2 V6 VTEC Coupe

3.2

5600

£55,000

£60,013

276

Preliminary Analysis:

According to the 30 data we selected, the graph above is produced by excel. As we can see from the graph above, the scatter plots indicates the general impression of the trend is positive. Although the graph is not strongly correlating due to limited samples, to some extent, it is suggested that mileage do influence the asking price of the cars. The formula of correlation should be used to prove whether the impression is right or wrong.

 X (Mileage) Y (Asking Price) X^2 Y^2 X*Y 1 12480 6,995 155750400 48930025 87297600 2 14283 17,900 204004089 320410000 255665700 3 15038 6,495 226141444 42185025 97671810 4 5000 7,995 25000000 63920025 39975000 5 11840 8,960 140185600 80281600 106086400 6 1344 7,995 1806336 63920025 10745280 7 15600 6900 243360000 47610000 107640000 8 16000 9,995 256000000 99900025 159920000 9 22000 7,495 484000000 56175025 164890000 10 26620 7,495 708624400 56175025 199516900 11 9922 9,995 98446084 99900025 99170390 12 15206 8,995 231222436 80910025 136777970 13 25000 11,995 625000000 143880025 299875000 14 12852 9,495 165173904 90155025 122029740 15 3048 8,495 9290304 72165025 25892760 16 9355 16,895 87516025 285441025 158052725 17 11200 8,995 125440000 80910025 100744000 18 15300 15,495 234090000 240095025 237073500 19 16549 8,495 273869401 72165025 140583755 20 17900 9,995 320410000 99900025 178910500 21 22000 15,495 484000000 240095025 340890000 22 24000 14,795 576000000 218892025 355080000 23 26000 10,450 676000000 109202500 271700000 24 9471 10,995 89699841 120890025 104133645 25 15300 15,495 234090000 240095025 237073500 26 20000 14,495 400000000 210105025 289900000 27 23000 14,695 529000000 215943025 337985000 28 25963 19,950 674077369 398002500 517961850 29 26572 15,499 706071184 240219001 411839428 30 5600 55,000 31360000 3025000000 308000000 SUM 474443 383,944 9015628817 7163472176 5903082453

Correlation coefficient=n Σxy-(Σx)(Σy)/√(n Σx^2-( Σx)^2)(n Σy^2-( Σy)^2)=0.091652

To find the relationship between mileage and price, we should judge whether there is correlation or not. The coefficient of correlation should be compared with critical point. The critical point of 30 data is 0.3061 and obviously, 0.091652 is smaller than 0.3061. Therefore, no strong correlation is found between mileage and price. The correlation between mileage and price is weak probably due to some other factors which affect the asking price.

2. New price and Power

Conclusion

Secondly, some significant correlation can be found between the current new prices and maximum powers of cars. For the relationship between current new price and maximum power, my investigation showed that there is a strong correlation. As a result, maximum power is an important determinant factor which influences the current new price. The stronger the maximum power, the higher the new price of the cars. However, due to the limitation of the samples and original data, the result cannot be to be representative. And there are also other factors are able to influence the price of cars, such as the colours, design, brand, taxation, fuel saving, and different agencies.

In spite of the limitations, we can still examine the general trend and predict the price of cars. Regression analysis might help car dealers to set the price and it might let the consumers gain some knowledge of price depreciation.

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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