Applied Statistics on letting of property in Nottingham, UK

Applied Statistics

Applied Statistics
Data
The data analyzed in this study contains information on letting of property in
Nottingham, United Kingdom. The data, Nottingham Data, was collected for 1889 observations
on 19 attributes of the properties. These attributes are Type, Address, Price, Furnish, Bedrooms,
Reception, Bathrooms, Longitude, Latitude, URL, Agent, dist 1, Station 1, dist 2, Station 2, dist
3, School 1, dist 4, School 2. The data preparation process involved the reduction in the variables
to focus on the variables of interest for this study; Type, Price and Furnish. This was achieved
using the code in Table 1: Variable Selection below
Table 1: Variable Selection
The table below, Table 2: Summarized Variables Description gives a summarized description of
the variables of interest in this study.
Table 2: Summarized Variables Description
Name Nature Scale
Type Categorical in nature with 18
levels: Bungalow, Detached
House, Detached Bungalow,
End Terrace House, Farm
House, Property, Linked
Detached House, Flat,
Maisonette, Room, Studio,
Nominal Scale
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Applied Statistics
Semi Detached Bungalow,
Shared Accommodation,
Semi Detached House, Town
House, Terraced House,
Mobile/Park Home and
Terraced Bungalow.
Price Numerical in Nature Ratio Scale
Furnish Categorical in Nature with 7
levels: Furnished, Un-
Furnished, Student Friendly,
Part Furnished, Furnished or
Unfurnished, Available from
13th Nov 2019 and Available
from 10th Dec 2019.
Nominal Scale
The data cleaning process first involved the checking and handling of NULL entries. This
was achieved using the code in Table 3: Checking and Handling of NULL Entries below. From
the table, only the Furnish variable had NULL entries totaling to 108, determined by combining
the sum () and is.na () functions. Compared to the sample size of the Nottingham Data of 1889,
omitting the 108 observations containing NULL entries for the Furnish variable will not affect
the analysis results, since it is a small number of observations. The resultant dataset after the
omission of the 108 observation, using the na.omit () function, is a sample of 1781 observations.
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Applied Statistics
Table 3: Checking and Handling of NULL Entries
The second stage of the data cleaning process involved the conversion of the Price data
variable from a character variable to a numeric variable. This was achieved using the code in
Table 4: Variable Conversion below. In this conversion, the actual price of the letting property
was separated from the currency initials, GBP, using the function sub (). The resulting price was
then converted from character form to numeric form using the as.numeric () function.
Table 4: Variable Conversion
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