• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month
Page
1. 1
1
2. 2
2
3. 3
3
4. 4
4
5. 5
5
6. 6
6
7. 7
7
8. 8
8

# Data Analysis of American House Price

Extracts from this document...

Introduction

American House Price Analysis

1. Introduction _________________________________________________________________

2. Executive Summary ______________________________

3. Introduction ____________________________________

4. Statistical Analysis _______________________________

4.1 - Overall Distribution of the house price  ________________________________________

4.2 - Investigation of Factors affecting the house price   ______________________________

4.3 - Investigation of Factors affecting the house price  ________________________________

5. Conclusion ____________________________________

6. Recommendations _______________________________

Notes   ____________________________________________

1. Terms of Reference

This report is the outcome of an analysis and investigation of American house price in order to consider which factor influence the price. It is submitted as my project for Essential Data Analysis module on the Business Studies Programme.

1. Executive Summary

The data was investigated using the software Minitab ver. 14. This program is very useful for analyzing big data set faster and easier. Through Minitab were created a graph for each requested point. In order o make the graph more understandable, it is provided a table with the more relevant statistic information. This allows a more comprehensive and understandable reading of the report and an easier and more efficient comparison among 2 or more variables in order to make a proper analysis. Correlation and Regression analysis was applied in order to establish the relationship between the price with the size and the distance to the nearest large town.

The data set given as a sample to analyse contain data collected of 100 houses in America from 5 different township numbered from 1 to 5.

Middle

100. As a result, 45% of the houses analysed does not have a pool.

The Graph 3 shows the proportion of the houses with a pool and a garage. By looking at the table it is clear that the majority of the houses with a pool have also a garage, with 58,18% (32 out of 55 houses with a pool); while 41,82% (23 out of 55) houses with a pool do not have a garage.

However, for houses without a pool, the proportion of houses without a garage is higher than houses with a pool where 82,22% (37 out of 45 houses) do not have a pool nor a garage.

It is evident from Graph 4 that the proportion of the houses with a pool is not the same in all the 5 townships.

In township 5, all the houses (100%) have a pool; followed by township 4 with a 94,4% of the houses. On the other extreme there is township 1 with only 13,33%  (2 out of 15 houses) have a pool, followed by township 2 with 22,22% (6 out of 27 houses). As table 4 shows the proportion of houses with a pool are in ascending order with the number of township: township 1 has the lowest percentage and township 5 has the highest. This could be a coincidence.

However, on the overall distribution, township 4 has the highest percentage of houses with a pool, with 32,73% (18 out of 55 total house

Conclusion

There is a slightly higher demand for houses with a pool.If the investor decides to buy a house with a pool, it is suggestible to have a garage as well. Otherwise it is more convenient to have a house without any of the two.If the house is in township 3 to 5, it is highly recommend to have a pool, especially for the last one.The pool will make a huge difference for the value of the house. The value will rise by about 75% if it has a pool.The bigger the house is, the more it values. However, houses with less than 1900sqrft are not very demanded. There is a medium demand for houses with a bigger size.Desirability scale 6 has a highest average and median price and it had a good demand.It is not relevant the distance between the house and a big city.

NOTES:

All the figures used to refer to the price are expressed as thousands of dollars (\$ ,000).

In order to determinate the demand, it has been used the assumption that the higher frequency has a higher demand. For example: in township 5 all the houses have a pool. It implies that everybody in that area demand and is willing to buy a house with a pool. Thus there is a very high demand.

This student written piece of work is one of many that can be found in our AS and A Level Probability & Statistics section.

## Found what you're looking for?

• Start learning 29% faster today
• 150,000+ documents available
• Just £6.99 a month

Not the one? Search for your essay title...
• Join over 1.2 million students every month
• Accelerate your learning by 29%
• Unlimited access from just £6.99 per month

# Related AS and A Level Probability & Statistics essays

1. ## The mathematical genii apply their Statistical Wizardry to Basketball

Lee took 269 shots and Dom accomplished 345 shots to score 80 baskets. Does this imply that Lee is more accurate? According to the expected mean values and the probabilities of scoring for each model it reinforces Lee's success where all three tests are in his favour.

2. ## Investigating the Relationship Between the Amount of Money a Football Club Receives and its ...

This is a problem because it makes the results of plotting a scatter graph misleading. If a scatter graph is plotted of money Vs. league position, problems could arise. For example, a team finishing 20th in Division 1 (24 teams)

1. ## I am investigating how well people estimate the length of a line and the ...

0.4 26 2 0.5 33 1 0.5 33 1 0.5 33 2 0.5 33 0.2 1.3 86 Table leading onto box plot for hypothesis 2 (Girls will be better at guessing the size of a short line than boys) Year 7 and 10 Males 1.5 0 0 1.5 0 0

2. ## I have been given the task of finding what affects the price of a ...

* For mileage I believe there will be a very strong negative correlation as the mileage increases the price will decrease. * For insurance group I believe there will be a weak negative correlation as the higher the insurance group the price will decrease but not by much.

1. ## Statistics. The purpose of this coursework is to investigate the comparative relationships between the ...

Peugot 406LX 13975 5795 3 53000 1 63 Volkswagen Golf GTi 16139 6995 5 35000 2 64 Ford Focus 14505 8800 2 7200 1 65 Ford Puma 13230 8250 3 34000 1 66 Peugot 206 9125 7500 1 18000 1 67 Peugot 406 17490 7500 1 17500 1 68 Honda

2. ## Design an investigation to see if there is a significant relationship between the number ...

This factor may vary throughout the year. For example, salt content will be relatively low after heavy rainfall. This will cause the rate of photosynthesis to increase, as the water potential of the sea will be less negative than that inside the algae, causing water to diffuse into cells of the seaweed, ready for photosynthesis.

1. ## Statistic: Is reading age a predictor for future attainment?

5 13.91 28-Sep - 87 2 9.00 28-Sep - 87 4 13.92 19-Sep - 87 6 11.00 5-Sep - 87 5 12.91 26-Sep - 87 6 12.50 Total of students = 16 Total = 77 77 / 16 = 4.8125 which The mean => 4.8125 * I am going to

2. ## Anthropometric Data

This also shows children with the foot breadth of about 52-55 (mm) are fairly grouped together where has foot breadth of 56 (mm) onwards or less are more spread out. Diagram 1 After plotting the scatter graph I'm able to have a visual impression of how the points lie, it

• Over 160,000 pieces
of student written work
• Annotated by
experienced teachers
• Ideas and feedback to
improve your own work