Prices of Property and Housing - Statistical Analysis

This statistical analysis report covers the prices of property and housing in Coastal City 1 of State B and Coastal City 2 of State A. It includes graphical outcomes, analysis, and results. The report is for MAT10251 Statistical Analysis course at Southern Cross University.

Running Head: PRICES OF PROPERTY AND HOUSING
SOUTHERN CROSS UNIVERSITY
School of Business and Tourism
MAT10251 Statistical Analysis
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PART A
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PRICES OF PROPERTY AND HOUSING
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MAT10251 – Statistical Analysis
Project Part A
Enter your sample number below
Sample Number (last digit of your student ID number) 7
Level of Significance 5%
Confidence Interval 95%

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PRICES OF PROPERTY AND HOUSING
Table of Contents
Reflection of Previous Assignment:................................................................................................4
Self-Marking Sheet:.........................................................................................................................4
Introduction......................................................................................................................................5
Graphical Outcomes:.......................................................................................................................5
Analysis and Results:.......................................................................................................................7
References:......................................................................................................................................9
Appendix:......................................................................................................................................10

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Reflection of Previous Assignment:
The previous assignment was reported incomplete from the end of assessor. The table of
summary or descriptive statistics was not included that are added in the present assignment file.
Frequency polygons or histograms was not properly carried out previously. Now, those
visualisations properly added. The marking was mot good for such types of major error.
Self-Marking Sheet:
The assessment of the file was –
Max Marks Recommended
Marks
Cover sheet not completed correctly
Format incorrect, including name
Statistical Calculations
Graph
Summary Statistics
Total Descriptive Statistics 0.0 0.0
Report
Introduction and data
Comments on graph
Comments on summary statistics
Difference in measures of central
tendency
Structure, grammar and spelling
Total Report 0.0 0.0
Total 0.0 0.0

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Introduction
The considered data displays the various aspects of rooms such as prices, number of
bedrooms, number of bathrooms, number of garages and types of rooms. The data of rooms of
houses are based on of coastal city 1 of State B. The pricing data of houses involves both the
coastal city 1 of State B and coastal city 2 of State A.
Graphical Outcomes:
It is known to all that – One image is more than thousand words. Graphical displays are
more vibrant than expression to visualize the inherent trends in the sample or the population. The
following graphs are executed with the help of all the 125 undertaken samples.
Figure 1: Histogram displays frequency distribution of Housing prices in $000 of Coastal City 1 State B
100-199 200-299 300-399 400-499 500-599 600-699 700-799 800-899 900-999 1000-
1099 1100-
1199 1200-
1299
0
5
10
15
20
25
30
35
40
45
Frequency Distribution of House Prices in Coastal City 1 of State B
Classes
Frequencies
Figure 2: Frequency polygon displays the cumulative frequency distribution of Housing prices in $000 of Coastal City 1 State B

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PRICES OF PROPERTY AND HOUSING
100-
199 200-
299 300-
399 400-
499 500-
599 600-
699 700-
799 800-
899 900-
999 1000-
1099 1100-
1199 1200-
1299 Total
0
20
40
60
80
100
120
140
Cumulative frequency distribution of House Prices in Coastal City 1 of State B
Classes
Cumulative frequencies
Figure 3: Histogram displays frequency distribution of Housing prices in $000 of Coastal City 2 State A
100-199 200-299 300-399 400-499 500-599 600-699 700-799 800-899 900-999 1000-
1099 1100-
1199 1200-
1299
0
5
10
15
20
25
30
Frequency Distribution of House Prices in Coastal City 2 of State A
Classes
Frequencies
Figure 4: Frequency polygon displays the cumulative frequency distribution of Housing prices in $000 of Coastal City 2 State A

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PRICES OF PROPERTY AND HOUSING
100-199 200-299 300-399 400-499 500-599 600-699 700-799 800-899 900-999 1000-
1099 1100-
1199 1200-
1299
0
20
40
60
80
100
120
140
Cumulative frequency distribution of House Prices in Coastal City 2 of State A
Classes
Cumulative frequencies
Analysis and Results:
Out of 125 surveyed houses 88 houses (70.4%) are of “House” type and 37 houses
(29.6%) are of “Unit” type (Leech, Barrett and Morgan 2013). It is 95% evident that the
estimated proportion of “Unit” type house lies in the interval of 0.216 and 0.376.
It was also hypothecated that the average price of houses of Coastal city 1 of State B is
greater than 500 units $000. However, as per testing of hypothesis, the basic assertion of equality
of average prices of houses with 500 units $000 is found to be true (De Winter 2013). Hence, the
hypothesis of the research is found to be false and interpreted that the average prices of houses is
not greater than 500 units $000.
As the internal area in m2 has increased, the prices of houses in both the cities of both the
states has increased rapidly. Therefore, the internal area has direct positive relationship with the
housing prices. Most of the houses in city 1 of state B are priced below $800,000. A few houses
cost over $700,000 in the coastal city 2 of state A.
Average is the most common measure of central tendency that simply represents a
population (Park 2015). Range is a simple measure that displays the bounds in which data values
lies. Greater range means higher spread of the data (Pituch, Stevens and Whittaker 2013). The
average price of houses for coastal city 1 of state B is 499.832 in $000 and the average price of
houses for coastal city 2 of state A is 482.918 in $000.
The average internal area is 153.2 m2. Most of the people has 3 bedrooms, 2 bath rooms and
2 garages. The minimum internal area of any house is 53.7 m2 and maximum internal area of any
house is 328.2 m2 with the calculated range 274.5 m2. Any house has minimum 1 bed room, 1
bathroom and no garage. On the other hand, any house has maximum 6 bed rooms, 4 bathrooms
and 8 garages. Greater number of bathrooms, bedrooms, garages and larger internal area
generally show that the house in both the cities cost higher. Coastal city 2 of State A has greater
range of prices of houses than Coastal city 1 of State B.
The same house with equal internal area and equal number of bed rooms, bathrooms or
garages has higher price in Coastal city 2 of State A (1209 units $000) than Coastal city 1 of
State B (1200 units in $000). The middle most value for prices of houses for Coastal city 1 of

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State B is 439 units in $000 and for Coastal city 2 of State A is 481 units in $000. The middle
value is very close to the average value of prices of houses in Coastal City 2 of State A.
However, for the houses of Coastal city 1 of State B, the difference of middle value and average
value is very significant. Therefore, according to the whole analysis it could be concluded that
the houses of Coastal city 2 of State A are more expensive than the houses of Coastal city 1 of
State B (Mendenhall and Sincich 2016).

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References:
De Winter, J.C., 2013. Using the Student's t-test with extremely small sample sizes. Practical
Assessment, Research & Evaluation, 18(10).
Leech, N., Barrett, K. and Morgan, G.A., 2013. SPSS for intermediate statistics: Use and
interpretation. Routledge.
Mendenhall, W.M. and Sincich, T.L., 2016. Statistics for Engineering and the Sciences.
Chapman and Hall/CRC.
Park, H.M., 2015. Univariate analysis and normality test using SAS, Stata, and SPSS.
Pituch, K.A., Stevens, J.P. and Whittaker, T.A., 2013. Intermediate statistics: A modern
approach. Routledge.

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Appendix:
Table 1: Descriptive statistics of house prices of two different cities of two different states
Table 2:Summary statistics of some other variables
Table 3: Pivot table of counts and percentages of “Type”
Table 4:One sample proportional Z-test

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Table 5: One-sample t-test for testing of hypothesis of house prices of Coastal city 1 of State B