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Application of number: level 3 - Is House Buying a Good Idea or Not?

Extracts from this document...

Introduction

Application of number: level 3 IS House Buying - a Good Idea or Not? A table to show the data collated for the samples for other houses and first-time buyers' houses (in ascending order): Other houses Price (£) First-time buyers' houses Price (£) 1 32,500 1 18,500 2 35,000 2 29,950 3 37,950 3 37,500 4 39,950 4 39,500 5 44,950 5 39,950 6 46,250 6 39,950 7 54,950 7 39,950 8 64,950 8 41,950 9 64,950 9 43,950 10 65,000 10 44,950 11 71,950 11 46,500 12 74,950 12 46,950 13 82,500 13 49,950 14 87,500 14 50,950 15 89,950 15 51,950 16 94,950 16 51,950 17 110,000 17 52,500 18 110,000 18 52,995 19 131,950 19 53,000 20 139,500 20 54,950 21 145,000 21 54,950 22 145,000 22 55,950 23 149,950 23 57,950 24 175,000 24 69,500 25 175,000 25 69,950 26 195,000 26 70,950 27 195,000 27 72,950 28 210,000 28 79,500 29 215,000 29 79,950 30 249,950 30 84,950 The mean of a set of data is the sum of the values divided by the number of values. Mean = sum of values / number of values The range is a measure of spread and the range of a set of data is the difference: Greatest value - least value First-time buyers' houses: * Mean: Sum of house prices / 30 = £1,584,445 / 30 = £52,815 (to the nearest £) ...read more.

Middle

Then the lower quartile value should be subtracted from the upper quartile value. Interquartile range = Upper quartile - Lower quartile First-time buyers' houses: Interquartile range = £59,000 - £41,000 = £18,000 (to nearest £000) Other houses: Interquartile range = £148,000 - £62,000 = £86,000 (to nearest £000) All houses: Interquartile range = £90,000 - £45,000 = £45,000 (to nearest £000) The spread of the data can be determined by calculating the standard deviation. This is a useful measure of dispersion that is the square root of the variance. However a short simple formula can be used: (?x2 / n) - x2 In the following calculations x = house price value, x = mean of house price values, and n = no. of house price values in sample. First-time buyers' houses: Standard deviation = (?x2 / 30) - x2 = [(18,5002 + 29,9502 + ........ + 84,9502) / 30] - 52,8152 = £15299 (to nearest £) Other houses: Standard deviation = (?x2 / 30) - x2 = [(32,5002 + 67,5002 + ........ + 249,9502) / 30] - 111,1532 = £62305 (to nearest £) All houses: Standard deviation = (?x2 / 60) - x2 = [(18,5002 + 29,9502 + ........ +249,9502) / 60] - 81,9842 = £41252 (to nearest £) It can be deduced from the standard deviations that the spread of the prices for the sample of other houses is considerably higher than those of first-time buyers' houses and all houses. ...read more.

Conclusion

Each one consists of a box, representing the middle half of the data (between the quartiles), and two "whiskers" showing the more extreme values, above and below the quartiles. Accuracy of the graphs and charts were ensured by carrying out the following: * Using tick marks with lower intervals * Adding values above bars (on Excel software) and ensuring that they correspond to the data in each of the sample tables. According to the data for all houses the average house price in my survey (i.e. £81,984) is lower than the average house price for my region (i.e. £88,700). Evidently this difference is not very considerable. The difference could be due to the following: * Lack of demand as Leicester may have less to offer as a city (e.g. employment, education, leisure and recreation, low crime rates etc.) * Inaccuracies in selecting the house prices for the sample, which may be caused by not including enough data or ineffective selective sampling (due to data being selected too randomly). However the average house price (for all houses) in my survey generally tends be similar to that of the UK (£81,800). Generally I think that buying a house in my local area would be a good investment due to the following reasons: * Average Leicester house prices being only relatively slightly above the national average. * Leicester house prices having the tendency to increase relatively considerably by about 26% within 2 years. ...read more.

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