HND Computing: Business Analytics Report on Automobile Data

This coursework report presents a comprehensive business analytics analysis of the automobile industry, focusing on a dataset from the American Statistical Association Exposition in 1983. The report begins with an explanation of the analysis's expectations and benefits for the automobile industry. It details the tools, techniques, and methodologies employed, including statistical software for data analysis. The analysis includes finding descriptive statistics (minimum, maximum, mean, median, mode) for horsepower, summary statistics for horsepower, weight, and displacement, and graphical representations of these variables. Central tendency analysis, including standard deviation and bell curves, is performed. The report also conducts hypothetical mean and variance testing to compare horsepower across different nations and employs statistical hypothesis testing to examine the relationships between horsepower and weight, horsepower and engine displacement, and horsepower and the number of cylinders. Normality testing is incorporated in the hypothesis testing. The report concludes with findings from the data analysis, including recommendations for the automobile industry, and may include regression analysis findings.

Assignment Cover Sheet
Qualification Module Number and Title
Higher National Diploma in Computing &
Software Engineering
COM5221 - Business Analytics
Student Name & No. Assessor
Chanidu Heshan Perera
CL/HNDCSE/88/03
Chanuka Dombagahawatta
Hand out date Submission Date
2020/7/21 2020/8/30
Assessment type
Coursework
Duration/Length of
Assessment Type
3 weeks
Weighting of Assessment
100 %
Learner declaration
I, ………………………………………….<name of the student and registration number>,
certify that the work submitted for this assignment is my own and research sources are fully
acknowledged.
Marks Awarded
First assessor
IV marks
Agreed grade
Signature of the assessor Date
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FEEDBACK FORM
INTERNATIONAL COLLEGE OF BUSINESS & TECHNOLOGY
Module:
Student:
Assessor:
Assignment:
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Strong features of your work:
Areas for improvement:

Acknowledgement
I would like to convey my special thanks of gratitude my lecturer, Mr. Chanuka Dombagahawatta
who guided me throughout this research project. Without his valuable guidance, this project
would not have been successful one. Further, I would also like to thank our institute for having
high quality library facilities with suitable guidebooks suit our needs of completing the project.
At last, I would also like to thank my parents and friends who helped a lot to finalizing this
assignment within a short time period.
Thank you.
Chanidu Heshan–03,
HND Batch 88
Course work
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Marks Awarded:

Coursework
Learning outcomes covered
 Understand Business Analytics methodologies, tools and the techniques
 Evaluate business advantages produced by business analytics.
 Be able to perform a business analysis
 Be able to propose solution for a business problem or creating opportunity using
appropriate business analytics methodologies, tools and the techniques
Scenario and the Task
Introduction
The Business Analytics subject domain is considered to be one of the major area
where most of the companies and various profitable and non- profitable institutions consider for
achieving the best decision support in their respective various operations life cycles. Because of
the economy is growing rapidly to achieve tangible and intangible as well as financial and non-
financial targets all most all the government and private organizations required to consider
precision and accuracy of their management decisions in all major three levels; operational,
tactical and strategic. The amount of information generated in modern agile economic
environment is very high and nature of consistency also highly varies within short time frame.
Because of the fact that utilization of big data analysis considered as one of the prime concern to
deal with the data to produce credible and valuable information. During the process of
conversion data to decision supportive information it is very important use different data analysis
methods, techniques and tools that are comprehensively explained in data analytics. The high
importance of data analytics subject elements leads to include those in modern management
information systems and decision support information systems for enabling online analytical
processing to incorporate with connected operational databases, data marts and data warehouses.
In performing big data analysis, it is very important to use good statistical software. At present
there are many such products available under generic or bespoke software category considering
open source or closed source. Usually open source products are financially feasible for many
organizations compared to closed source products. At present big data analysis rapid growth
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identifiable in open source category with frequent versions and many feature extensions
compared to closed source. On the other hand, side much reliable many software products have
been released by industry pioneer solution providers. Therefore, section of the best product for
data analysis is also need to be done wisely by relevant authorities of organizations for their
objectives to be precisely achieved.
SCENARIO
The automobile industry considered as one of the major contributing industries for world’s
economic and technology development. Specially American, German, European and Japanese
companies pioneered for the automobile revolution of the world by introducing innovative
engineering technologies and methodologies for automobile manufacturing.
Manufacturing durable, hard & expensive vehicles was the practice initially but gradually
changed it to more comfortable, safe, high-speed and affordable vehicle manufacturing.
The dataset comprises of information about various types of automobiles manufactured by
Americans, Europeans and Japanese companies. The information includes vehicle’s Engine
displacement, cylinders, horsepower, weight, acceleration, year, origin and name. Based on the
information available it is feasible for understand performance variations and associations of
motor vehicles depending on the nature of engine, transmission subsystem, fuel subsystem,
electrical subsystems, breaking subsystem, passenger subsystem and climate control subsystem.
Any changes in those directly influence to have performance variations in the motor vehicle.
As a data analyst of newly opening automobile manufacturing company in Sri Lanka you are
required to prepare a report comprises of findings of the data analysis based on the dataset
associated with this.
Survey Data Dictionary
Variable name Description
mpg miles per gallon
cylinders Number of cylinders between 4 and 8
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The original dataset has been provided to you as a separate data file labeled “Auto.csv” which
was used by American Statistical Association Exposition in 1983.

displacement Engine displacement (cu. inches)
horsepower Engine horsepower
weight Vehicle weight (lbs.)
acceleration Time to accelerate from 0 to 60 mph (sec.)
year Model year (modulo 100)
origin Origin of car (1. American, 2. European,
3. Japanese)
name Vehicle name
Tasks
1. Provide an in detailed explanation about the expectation of the analysis and benefits generated
for the automobile industry.
(5Marks)
2. Explain tools, techniques and methodologies going to use for the analysis.
(6Marks)
3. Find out minimum, maximum, mean, median, mode of horsepower of the vehicles. (6 Marks)
4. Find out summary statistics of horsepower, weight and displacement of the vehicles. (6
Marks)
5. Graphically represent horsepower, weight and engine displacement of vehicle models during
the mentioned period of the survey. (10 marks)
6. Conduct central tendency analysis for horsepower, weight and engine displacement and find
out standard deviation of those. Represent finding graphically using bell curves. (12 Marks)
7. Using most suitable statistical hypothetical mean and variance testing justify whether what
nations manufactured differentiated horsepower oriented motor vehicles. (10 Marks)
8. Using statistical hypothetical testing prove, whether there is a statistically significant
relationship exist with horsepower and weight in vehicle models. (10 marks)
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9. Using statistical hypothetical testing prove, whether there is a statistically significant
relationship exist with horsepower and engine displacement in vehicle models. (10 marks)
10. Using statistical hypothetical testing prove, whether there is a statistically significant
relationship exist with horsepower and number of cylinders in the engine in vehicle models.
(10 Marks)
11. Write a conclusion based on the findings of the data analysis based on above findings and
suggest necessary recommendations. (15 Marks)
 Question no 8,9 and 10 should be incorporated with normality testing
 Note: The conclusion can include the findings of suitable regression analysis as well.
Marking Scheme
Task-1 contains 5 marks
Criteria
Marks
Out of 5
Fail
Not Explained the scenario and not included the benefits provided
by the analysis for the selected company/institution etc.. 0-1
Pass
Explained the scenario. 1-2
Good
Explained the benefits provided by the analysis for the selected
company/institution etc. 2-3
Excellent
Well explained the scenario and the benefits provided by the
analysis for the selected company/institution. 3-5
Task-2 contains 6 marks
Marks
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Criteria Out of 6
Fail
Not Stated tools, techniques and methodologies going to be used
for analysis
0-1
Pass
Stated tools, techniques and methodologies going to be used for
analysis
1-2
Good
Stated tools, techniques and methodologies going to be used for
analysis. Explained the mentioned tools, techniques and
methodologies.
2-4
Excellent
Stated tools, techniques and methodologies going to be used for
analysis. Well explained the mentioned tools, techniques and
methodologies.
4-6
Task-3 contains 6 marks
Criteria
Marks
Out of 6
Fail
Not used minimum, maximum, mean, median, mode functions to
get respective statistics of provided data 0-2
Pass
Used minimum, maximum, mean, median, mode functions to get
respective statistics of provided data
2-4
Good
Used minimum, maximum, mean, median, mode functions to get
respective statistics of provided data and got an accurate set of
results and explained briefly about what obtained.
4-5
Excellent
Used minimum, maximum, mean, median, mode functions to get
respective statistics of provided data and got an accurate set of
results and well explained about what obtained.
5-6
Task-4 contains 6 marks
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Criteria
Marks
Out of 6
Fail
Not included summery statistical data using summary statistical
function. 0
Pass
Included summery statistical data using summary statistical
function.
6
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Task-5 contains 6 marks
Criteria
Marks
Out of 10
Fail
Not Included bar chart/pie chart/ to represent data provided
graphically. 0-1
Pass
Included bar chart/pie chart/ to represent data provided
graphically.
1-4
Good
Included bar chart/pie chart/ to represent data provided graphically
and included brief information about what is represented in the
charts.
4-7
Excellent
Included bar chart/pie chart/ to represent data provided graphically
and included well described information about what is represented
in the charts.
7-10
Task-6 contains 12 marks
Criteria
Marks
Out of 12
Fail
Not Included bell curves based on data provided 0-1
Pass
Included bell curves based on data provided 1-6
Good
Included bell curves based on data provided and
Described information represented in the charts briefly 6-10
Excellent
Included bell curves based on data provided and
Well described information represented in the charts briefly
10-12
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Task-7 contains 10 marks
Criteria
Marks
Out of 10
Fail
Not Inclusion of hypothetical statement considering null
hypothesis and alternative hypothesis 0-1
Pass
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on mean and variance analysis test. Yet some errors exists.
1-5
Good
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis. Good statistical justification of result
based on mean and variance analysis test.
6-7
Excellent
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis. Excellent statistical justification of
result based on mean and variance analysis test.
7-10
Task-8 contains 10 marks
Criteria
Marks
Out of 10
Fail
Not Inclusion of hypothetical statement considering null
hypothesis and alternative hypothesis 0-1
Pass
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test
1-5
Good
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test
6-7
Excellent
Inclusion of hypothetical statement considering null hypothesis 7-10
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and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test.
Results findings and justifications done excellent manner.
Task-9 contains 10 marks
Criteria
Marks
Out of 10
Fail
Not Inclusion of hypothetical statement considering null
hypothesis and alternative hypothesis 0-1
Pass
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test
1-5
Good
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test
6-7
Excellent
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test.
Results findings and justifications done excellent manner.
7-10
Task-10 contains 10 marks
Criteria
Marks
Out of 10
Fail
Not Inclusion of hypothetical statement considering null
hypothesis and alternative hypothesis 0-1
Pass
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
1-5
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based on correlation analysis test
Good
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test
6-7
Excellent
Inclusion of hypothetical statement considering null hypothesis
and alternative hypothesis and statistical justification of result
based on correlation analysis test supported by normality test.
Results findings and justifications done excellent manner.
7-10
Task-11 contains 15 marks
Criteria
Marks
Out of 15
Fail
Not Provided conclusion based on statistical findings done based
on hypothetical testing. 0-1
Pass
Provided conclusion based on statistical findings done based on
hypothetical testing.
1-5
Good
Provided conclusion based on statistical findings done based on
hypothetical testing. The conclusion should comprise of sub
conclusions of question no 07, 08, 09 and 10.
5-10
Excellent
Provided conclusion based on statistical findings done based on
hypothetical testing. The conclusion should comprise of sub
conclusions of question no 07, 08, 09 and 10. The student provided
recommendation to support improvements of the organization.
Applications of suitable regression analysis findings are highly
anticipated.
10-15
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Table of content
Contents
Task01.........................................................................................................................................................3
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Task 01
Data analyzing:-
Data analysis is a process which depend on methods and techniques to taking raw data, mining
for insights that are relevant to the business’s primary goals, and break down into this
information to transform metrics, facts, and figures into initiatives for enhancement.
There are number of approach for data analysis, widely based on two core areas: quantitative
data analysis methods and data analysis methods in qualitative research. (Durcevic, 2020)
Purpose of analyzing:-
Data analysis is used by small businesses, retail companies, in medicine, and even in the world
of sports. It's a global language and more significant than ever before. It looks like a progressive
approach but data analysis is actually just a rare ideas put into practice.
(GRANT, 2020)
Methods of data analysis:-
Text Analysis
Text Analysis is also mentioned like Data Mining. It is a method to determine a pattern in large
data sets by databases or data mining tools. It used to convert raw data into business information.
Business Intelligence tools are present in the market which is used to take strategic business
decisions. Overall it deals a way to excerpt and examine data and deriving patterns and lastly
clarification of the data.
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Statistical Analysis
Statistical Analysis displays "What happen?" by using past data in the form of dashboards.
Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of
data. It analyses a set of data or a sample of data. There are two classifications of this type of
Analysis - Descriptive Analysis and Inferential Analysis.
Descriptive Analysis
Analysis complete data or a model of brief numerical data. It shows mean and deviation for
continuous data whereas percentage and regularity for definite data.
Inferential Analysis
Analyses sample from complete data. In this kind of Analysis, you can find various different
conclusions from the same data by choosing different samples.
Predictive Analysis
Predictive Analysis displays "what is likely to happen" by using previous data. The simplest
example is like if previous year I bought two clothes based on my savings and if this year my
salary is growing double then I can buy four clothes. But of course it's not easy like this because
you have to think about other conditions like chances of prices of clothes is increased this year or
maybe instead of clothes you want to buy a new bike, or you need to buy a house!
So now, this Analysis makes predictions about future products based on current or past data.
Forecasting is just an approximation. Its correctness is based on how much detailed information
you have and how much you dig in it.
Prescriptive Analysis
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Prescriptive Analysis combines the insight from all earlier Analysis to define which action to
take in a present problem or decision. Most data-driven businesses are utilizing Prescriptive
Analysis because predictive and descriptive Analysis are not enough to advance data
performance. Based on existing situations and problems, they analyze the data and make results.
Data Analysis process
The Data Analysis Process is nothing but gathering information by using a suitable application
or tool which allows you to explore the data and find a pattern in it. Based on that information
and facts, you can make decisions, or you can get ultimate conclusions.
Data Analysis consists of the following phases:
 Data Requirement Gathering
 Data Collection
 Data Cleaning
 Data Analysis
 Data Interpretation
 Data Visualization
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Data Requirement Gathering
First of all, you have to think about why do you need to do this data analysis? All you need to
find out the target or aim of doing the Analysis. You need to decide which kind of data analysis
you needed to do! In this phase, you have to decide what to analyze and how to measure it, you
need to understand why you are investigating and what measures you have to use to do this
Analysis.
Data Collection
After requirement gathering, you will get a clear idea about what things you need to measure and
what should be your results. Now it's time to gather your data based on requirements. Once you
gather your data, remember that the gathered data must be processed or organized for Analysis.
As you collected data from many sources, you must have to keep a log with a gathering date and
source of the data.
Data Cleaning
Now whatever data is collected may not be valuable or irrelevant to your goal of Analysis,
therefore it can be cleaned. The data which is collected may contain duplicate records, white
spaces or errors. The data can be cleaned and error free. This phase must be complete before
Analysis because based on data cleaning, your output of Analysis will be closer to your
predictable outcome.
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Data Analysis
After the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate
data, you may find you have the exact information you need, or you might need to collect more
data. During this phase, you can use data analysis tools and software which will help you to
understand, interpret, and derive conclusions based on the requirements.
Data Interpretation
After analyzing your data, it's lastly time to interpret your results. You can choose the way to
express or communicate your data analysis whichever you can use simply in words or perhaps a
table or chart. Then use the outcomes of your data analysis process to decide your best course of
action.
Data Visualization
Data visualization is very mutual in your day to day life; they often appear in the form of charts
and graphs. In other words, data shown graphically so that it will be easier for the human brain to
understand and process it. Data visualization often used to discover unknown facts and trends.
By observing relationships and comparing datasets, you can find a way to find out meaningful
information.
(guru99, 2020)
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Task 02
Technologies used for analyzing
The growing demand and significance of data analytics in the market have generated many
openings worldwide. It becomes slightly tough to shortlist the top data analytics implements as
the open source tools are more common, user-friendly and performance oriented than the paid
version. There are many open source tools which doesn’t require much/any coding and manages
to deliver better results than paid versions e.g. – R programming in data mining and Tableau
public, Python in data visualization. Following are some tools
R programming
R is the principal analytics tool in the industry and widely used for statistics and data modeling.
It can simply manipulate your data and present in different ways. It has exceeded SAS in many
ways like volume of data, performance and outcome. R compiles and runs on a wide variety of
platforms viz -UNIX, Windows and MacOS. It has 11,556 packages and allows you to browse
the packages by classes. R also provides tools to automatically install all packages as per user
necessity, which can also be well assembled with Big data.
Tableau Public
Tableau Public is a free software that joins any data source be it corporate Data Warehouse,
Microsoft Excel or web-based data, and generates data visualizations, maps, dashboards etc. with
real-time updates presenting on web. They can also be shared through social media or with the
client. It allows the access to download the file in different formats. If you need to see the power
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of tableau, then we must have very worthy data source. Tableau’s Big Data capabilities makes
them significant and one can analyze and visualize data improved than any other data
visualization software in the market.
Python
Python is an object-oriented scripting language which is easy to read, write, maintain and is a
free open source tool. It was developed by Guido van Rossum in late 1980’s which helps both
functional and structured programming methods. Python is easy to study as it is very similar to
JavaScript, Ruby, and PHP. Moreover, Python has very good machine learning libraries viz.
Scikitlearn, Theano, Tensorflow and Keras. Additional important feature of Python is that it can
be assembled on any platform like SQL server, a MongoDB database or JSON. Python can also
handle text data very well.
SAS
Sas is a programming environment and language for data manipulation and a front-runner in
analytics, developed by the SAS Institute in 1966 and further developed in 1980’s and 1990’s.
SAS is simply accessible, managable and can analyze data from any sources. SAS announced a
large set of products in 2011 for customer intelligence and numerous SAS modules for web,
social media and marketing analytics that is widely used for profiling customers and prospects. It
can also predict their behaviors, manage, and optimize communications.
Apache Spark
The University of California, Berkeley’s AMP Lab, established Apache in 2009. Apache Spark
is a fast large-scale data processing engine and performs applications in Hadoop clusters 100
times quicker in memory and 10 times quicker on disk. Spark is built on data science and its
concept makes data science effortless. Spark is also popular for data pipelines and machine
learning models development.
Spark also includes a library – MLlib, that provides a progressive set of machine algorithms for
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repetitive data science techniques like Classification, Regression, Collaborative Filtering,
Clustering, etc.
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Rapid Miner
RapidMiner is a powerful combined data science platform established by the same company that
performs predictive analysis and other progressive analytics like data mining, text analytics,
machine learning and visual analytics without any programming. RapidMiner can incorporate
with any data source types, including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase,
IBM DB2, Ingres, MySQL, IBM SPSS, Dbase etc. The tool is very powerful that can generate
analytics based on real-life data transformation settings, i.e. you can control the formats and data
sets for predictive analysis.
KNIME
KNIME Established in January 2004 by a team of software engineers at University of Konstanz.
KNIME is leading open source, reporting, and combined analytics tools that allow you to
analyze and model the data through visual programming, it integrates various components for
data mining and machine learning via its modular data-pipelining concept.
(proschoolonline, n.d.)
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Task 03
Output
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Task 04
Output
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Task 05
Output
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Horse power bar chart
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Weight bar chart
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Displacement bar chart
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Task 06
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Output
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Bell curve horse power bar chart
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Bell curve weight bar chart
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Bell curve engine displacement bar chart
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Task 7
Output
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Task 08
Output
Task 09
Output
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Task 10
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Grant chart
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Reference
References
Durcevic, S., 2020. datapone. [Online]
Available at: https://www.datapine.com/blog/data-analysis-methods-and-techniques/
[Accessed 17th August 2020].
GRANT, A., 2020. makeuseof. [Online]
Available at: https://www.makeuseof.com/tag/what-is-data-analysis/
[Accessed 17th August 2020].
guru99, 2020. GURU99. [Online]
Available at: https://www.guru99.com/what-is-data-analysis.html#4
[Accessed 17th August 2020].
proschoolonline, n.d.. proschool. [Online]
Available at: https://www.proschoolonline.com/blog/top-10-data-analytics-tools
[Accessed 25th August 2020].
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