Business Analytics and Big Data Research Paper PDF 2023

University
Business Analytics and Big Data
By
Your Name
Date
Page 1 of 18
©<Your Name> 2019

Business Analytics and Big Data
Introduction
There are different factors that affect the selling price of a house or a unit.
These factors could either be the number of rooms, bathrooms, and
bedrooms, the availability and number of garages and the per unit area of
the rooms. The above factors can affect the price in either a positive or
negative manner. The purpose of this paper is to present the numeric
summaries and their appropriate visualizations for factors affecting the price
of a house or a unit and develop the best regression model that can be used
to predict the price of properties.
Background
Numerical summaries are numbers that are used to describe various
characteristics of data. They can be applied to both numeric and categorical
to explain the measures of central tendency or the measures of spread
(Foster, 2009). Measures of central tendency show the center point of the
data while measures of spread show the variability (Freund, 2014). On the
other hand, linear regression is a technique used in statistics to determine
the relationship between variables. Simple linear regression explains the
relationship between a single independent variable and a dependent variable
while multiple linear regression uses many independent variables (Linoff,
2011). An optimum model that is statistically significant should be selected
Page 2 of 18
©<Your Name> 2019

as the best model to explain the relationship between the dependent
variables and the predictor (Lock, 2013).
The Data
The data used was collected by a large national real estate company known
as safe-As-House Real Estate. It comprises of recent residential sales from
samples of non-capital cities and towns in State A. The non-capital cities are
Regional and Coastal cities one and two while the towns are the Coastal
towns 1 and 2. The data is collected by a different entity from that which is
performing the analysis and therefore it is a secondary type of data (Shao,
2010). The data comprises of six variables namely; Price, Internal Area,
Bedrooms, Bathrooms, Garages and Type. The variables price and internal
area are continuous numeric variables while the variable type is a
categorical variable (Freund, 2014). The variables bedrooms, bathrooms and
garages are categorical variables although they are coded as numeric to
represent the different labels of the respective variables. For example, the
digit 1 in bedroom could represent bedroom number one, while the digit 2
could represent bedroom number two (Levie, 2012).
Empirical Strategy
Numerical Summaries
Page 3 of 18
©<Your Name> 2019

The numerical summaries of the continuous numeric variables determined
are categorized into two groups. The first group is the measure of central
tendencies while the second group is the measure of dispersion or spread
(Jackson, 2016). The table below is a summary of the numeric summaries of
all the locations under study in state A.
Combined Numeric Summaries for continous Variables for All the Cities in State A
Mean Median Mode Standard Deviation Variance Range
Regional City 1 Price $000 320.81 298.00 269.00 110.60 12231.87 480.00
Internal Area m^2 140.41 141.50 90.40 43.49 1890.98 283.10
Regional City 2 Price $000 383.08 357.00 349.00 120.27 14465.08 590.00
Internal Area m^2 142.95 128.70 93.20 55.61 3092.50 222.00
Costal City 1 Price $000 458.64 399.00 429.00 212.95 45347.64 1138.00
Internal Area m^2 148.81 146.50 92.40 46.98 2207.09 222.60
Costal city 2 Price $000 494.58 473.00 580.00 221.82 49202.08 1210.00
Internal Area m^2 130.80 119.70 114.60 48.52 2353.96 182.80
Costal Town 1 Price $000 570.89 545.00 599.00 205.12 42074.76 1105.00
Internal Area m^2 167.36 162.20 273.00 61.62 3797.01 259.00
Coastal Town 2 Price $000 483.09 446.60 446.60 183.65 33728.45 851.50
Internal Area m^2 135.62 129.90 129.90 47.48 2254.04 206.40
From the table above, a property at Coastal Town 1 has a higher average
price compared to the rest of locations. The lowest mean price is exhibited in
a property situated in Regional city 1. Moreover, a property in Coastal Town
1 has a higher internal area compared to properties in other locations. The
property with the least average internal area is situated in Regional City 1. A
comparison of the mean, median and mode of the different locations show
that the distribution the prices in Regional cities and Coastal cities is
positively skewed since the mean is larger than the median and the mode
while the distribution of prices in Coastal towns is negatively skewed since
the mode is greater than the median and the mean. The distribution of
internal areas is positively skewed in Regional cities, Coastal cities and
Page 4 of 18
©<Your Name> 2019