Business Statistical Modeling

Business 1
Business Statistical Modeling
Name of Author
Name of Class
Name of Professor
Name of School
State and City of School
Date

Business 2
Introduction
The aim of this assignment is to give participants the skills that are relevant for data collection
and the analysis of the respective datasets. Uses of data range across several disciplines. Data is needed
for research in learning institutions and for this, the social sciences have their own software for
analyzing their own data, the SPSS software (Ho, 2017). Data is also needed by institutions outside the
learning field. Different governments carry out censors from time to time in order to know how much its
population has grown and what number of people is its population. This information can then be used to
do planning on matters resource allocation later in the next years before another sensor is conducted.
Similarly, business institutions are prone to the act of data usage. Business institutions collect and
analyze datasets from different sources to aid in making informed business decisions. Decisions which
are influenced by knowing which products are purchased more than others and all of that can be gotten
via data. Business use of datasets to make informed decisions is called business analytics (Mendoza,
Gallego-Schmid & Azapagic, 2019).
Business analytics which is abbreviated as BA is the use of the frequentative study of
organizational data through methods. This usually has an emphasis on statistical analysis. The sole
purpose for the use of business analytics is to aid in making data-driven decisions. Numbers do not lie
decisions made in numbers that are drawn from data would be more informed. This would lead to
higher optimization and automation of business processes. Most corporates treat data as assets and
only use it for competitive advantage (Laudon & Traver, 2016).
The assignment is typically business analytics related assignments as it will focus on the two types of
business analytics which are Business Intelligence and the deeper statistical analysis in drawing
conclusions from the data collected that is in relation to different fuel providers in Australia (Laursen &
Thorlund, 2016). This is the more reason for those engaged to apply learned theories of finding
numerical summaries, graph visualizations and construction of respective statistical hypotheses which
are later tested to better help in the interpretation of results.
Dataset 1 is a secondary dataset that was collected from the Australian open dataset website
that contains fuel checks information for different years and 2016 was the desired year from which
dataset 1 was picked. There are six variables in total and only two types; the string and the numerical
variables. There are four variables which are actually string and these are; Address, Suburb, Brand, and
FuelCode. The remaining Postcode and Price are both numerical variables. There are 1001 cases and
the very first case is the variables names case. Each case has up to six features represented by all the six
variables that are seen in each case has all information that relates to a fuel station and the price of that
respective fuel station (Higashinaka, Funakoshi, Kobayashi & Inaba, 2016).
Dataset 2 is a secondary dataset and I collected it from Australian public data and the actual
website from where it got collected from is; https://data.gov.au/dataset/ds-nsw-a97a46fc-2bdd-4b90-
ac7f-0cb1e8d7ac3b/details. After which it was edited and it has eight variables, three of which are
numeric and five are string variables. The cases are thirty in total which have eight characteristics as per
the respective variables. The dataset is full of limitations because it is more biased as opposed to the
first dataset because it is a small sample with only thirty cases to be analyzed which would only take a
few service stations as opposed to dataset 1.
Analysis of single variable in Dataset 1

Business 3
This section brings us to the analytical part of the price variable of dataset 1. For one to
understand how a dataset it numerically then a descriptive statistics must be run on the respective
dataset or data variable that is to be analyzed. The purpose of descriptive statistics is to determine the
numerical constants that aid in the quantitative and qualitative analysis of the datasets (Larson-Hall,
2015). There also needs to know how many data points are there and how many times does a data point
occur in the dataset. The descriptive statistics figure is as shown in table 1;
Figure 1
The mean and the median are a bit close showing that the data points are a bit deviated but not so far
from the centre. This is also supported by the small value that is there for standard deviation, a true
indication that data points are not extensively deviated from the centre. The data points though, as can
be seen from sample variance that stands at 13.48, mostly fall far apart from each other. The price that
most petrol service stations offer fuel for is the modal price which is at 129.9 (Otto, 2016).
Further distribution of the price variable is shown by a histogram graphical representation as
bellow in figure 2;