Unit 10 Assignment 3
Introduction: In today’s car market, businesses need to make smart decisions based
on reliable information. So in this report, it will looks at car registration trends over the
past three years to help a car dealership to make decisions which will help them to
choose right way to grow up in the market. The company has six locations in southern
England and is deciding whether to stick with its current car manufacturers or attached
with new car brands. To make the right decision, the directors need clear information
on which car manufacturers are growing on the market and which are having fewer
sales.
Analysing data is key to spotting trends, predicting future sales and reducing business
risks. This report uses official UK government records to show which car brands have
grown rapidly over the years and which have declined. It also applies different
statistical methods to provide strong evidence to support the company’s decision.
By using big data, the company can decide whether to keep its current brands or to
replace those that are underperforming on the market or introduce new way to sells
cars. This will help the business stay competitive and meet its customer demand.
Data Selection and Preparation
Describe the dataset source (UK Vehicle Licensing Statistics): The data for this
analysis comes from the UK Vehicle Licensing Statistics which is an official government
source that records new vehicle registrations across the UK. It includes detailed
information on registrations by manufacturer, model, fuel type and time period the
Vehicle has sold on the market. This dataset is publicly available on the UK Government
website (gov.uk) and is commonly used for industry research and policymaking.
Justification for Data Selection: A three-year period was considered for this
analysis to get a clear and accurate understanding of car registration data. Looking at
just one year of data would not show long-term market changes because the sales can
be affected by short-term factors like the economy, government incentives or supply
chain issues. A single year might show a trend weather its increase or decrease in sales
that does not reflect the real trend, leading to having wrong conclusions. On the other
hand, using ten years of data might include old trends that no longer match the current
market. The car industry changes quickly due to new technology, customer preferences
and government rules. Data from ten years ago may not be useful for making business
decisions today.
,By choosing three years, this analysis provides a good balance which will helps to
identify the real trends in car sales without being affected by short-term changes. It also
makes it easier to see which car manufacturers are growing or losing market share. A
three-year dataset reduces the impact of sudden sales increases or decreases caused
by major global events or special offers. This period also helps the company make
better predictions about the future and ensures that business decisions, like changing
manufacturers or expanding dealerships, are based on solid data rather than having
temporary changes.
Since the dataset includes many car manufacturers, not all of them are important for
this analysis. Some brands have very low sales and including them would make the
analysis more complicated without adding useful information. To keep things simple
and relevant, this report focuses on the top four manufacturers based on total new car
registrations over three years. These brands were chosen because they have a strong
trends in the market and play a key role in the car industry. So those car which have
more then 1000+ selling car in a year those are will be selected. Focusing on fewer
brands makes it easier to use statistical methods and provide clear recommendations
without having unnecessary data.
All these brands had strong sales on the market last three years, which shows the
higher customer demand. By comparing their sales over three years, the company can
see which brands are growing and which are declining. This will help the directors
decide whether to keep working with these brands or to replace those that are not
performing well or add new ones to the dealership.
Why Excel Was Chosen for Data Analysis?
Excel was used for data analysis because it's had many tools and popular tools for
working with data. It helps to organise, sort, and study information in an easy way. One
of reason for choosing Excel is because it's had simple design, which makes it easy to
use without needing advanced mathematics and computer skills. It also has sorting
and filtering tools which can help to arrange large amounts of data, remove
unnecessary information and focus on important data, like the most popular car
models.
Another reason for using Excel is that its have the ability to analyse the data in any
format. It has mathematical functions like SUM, AVERAGE, MEDIAN and STANDARD
DEVIATION that can help to show the patterns and changes in the data. Also, Pivot
Tables make it easy to summarise and compare large amounts of information at the
same time, helping to find trends in car sales over three years.
, Excel also have many options like graph and chart options, like bar charts, line graphs
and pie charts, which can be able to show the data different way. These graphics will
make it easier to see trends, like which car brands are selling more or less, helping the
company make better decisions.
Excel also helps check for mistakes in the data to keep it accurate. Features like
conditional formatting allow important trends or unusual data to be highlighted, making
analysis easier.
Explain the data cleaning process:
The data is used for this analysis was gathered from the UK Government’s Vehicle
Licensing Statistics Database, which provides official records of all the vehicle
registrations across the United Kingdom. From this database, two specific datasets
were downloaded: VEH0171 and VEH0181.
VEH0171 contains detailed information about the number of new vehicle registrations
by manufacture and its model, while VEH0181 provides the information of the car
Introduction: In today’s car market, businesses need to make smart decisions based
on reliable information. So in this report, it will looks at car registration trends over the
past three years to help a car dealership to make decisions which will help them to
choose right way to grow up in the market. The company has six locations in southern
England and is deciding whether to stick with its current car manufacturers or attached
with new car brands. To make the right decision, the directors need clear information
on which car manufacturers are growing on the market and which are having fewer
sales.
Analysing data is key to spotting trends, predicting future sales and reducing business
risks. This report uses official UK government records to show which car brands have
grown rapidly over the years and which have declined. It also applies different
statistical methods to provide strong evidence to support the company’s decision.
By using big data, the company can decide whether to keep its current brands or to
replace those that are underperforming on the market or introduce new way to sells
cars. This will help the business stay competitive and meet its customer demand.
Data Selection and Preparation
Describe the dataset source (UK Vehicle Licensing Statistics): The data for this
analysis comes from the UK Vehicle Licensing Statistics which is an official government
source that records new vehicle registrations across the UK. It includes detailed
information on registrations by manufacturer, model, fuel type and time period the
Vehicle has sold on the market. This dataset is publicly available on the UK Government
website (gov.uk) and is commonly used for industry research and policymaking.
Justification for Data Selection: A three-year period was considered for this
analysis to get a clear and accurate understanding of car registration data. Looking at
just one year of data would not show long-term market changes because the sales can
be affected by short-term factors like the economy, government incentives or supply
chain issues. A single year might show a trend weather its increase or decrease in sales
that does not reflect the real trend, leading to having wrong conclusions. On the other
hand, using ten years of data might include old trends that no longer match the current
market. The car industry changes quickly due to new technology, customer preferences
and government rules. Data from ten years ago may not be useful for making business
decisions today.
,By choosing three years, this analysis provides a good balance which will helps to
identify the real trends in car sales without being affected by short-term changes. It also
makes it easier to see which car manufacturers are growing or losing market share. A
three-year dataset reduces the impact of sudden sales increases or decreases caused
by major global events or special offers. This period also helps the company make
better predictions about the future and ensures that business decisions, like changing
manufacturers or expanding dealerships, are based on solid data rather than having
temporary changes.
Since the dataset includes many car manufacturers, not all of them are important for
this analysis. Some brands have very low sales and including them would make the
analysis more complicated without adding useful information. To keep things simple
and relevant, this report focuses on the top four manufacturers based on total new car
registrations over three years. These brands were chosen because they have a strong
trends in the market and play a key role in the car industry. So those car which have
more then 1000+ selling car in a year those are will be selected. Focusing on fewer
brands makes it easier to use statistical methods and provide clear recommendations
without having unnecessary data.
All these brands had strong sales on the market last three years, which shows the
higher customer demand. By comparing their sales over three years, the company can
see which brands are growing and which are declining. This will help the directors
decide whether to keep working with these brands or to replace those that are not
performing well or add new ones to the dealership.
Why Excel Was Chosen for Data Analysis?
Excel was used for data analysis because it's had many tools and popular tools for
working with data. It helps to organise, sort, and study information in an easy way. One
of reason for choosing Excel is because it's had simple design, which makes it easy to
use without needing advanced mathematics and computer skills. It also has sorting
and filtering tools which can help to arrange large amounts of data, remove
unnecessary information and focus on important data, like the most popular car
models.
Another reason for using Excel is that its have the ability to analyse the data in any
format. It has mathematical functions like SUM, AVERAGE, MEDIAN and STANDARD
DEVIATION that can help to show the patterns and changes in the data. Also, Pivot
Tables make it easy to summarise and compare large amounts of information at the
same time, helping to find trends in car sales over three years.
, Excel also have many options like graph and chart options, like bar charts, line graphs
and pie charts, which can be able to show the data different way. These graphics will
make it easier to see trends, like which car brands are selling more or less, helping the
company make better decisions.
Excel also helps check for mistakes in the data to keep it accurate. Features like
conditional formatting allow important trends or unusual data to be highlighted, making
analysis easier.
Explain the data cleaning process:
The data is used for this analysis was gathered from the UK Government’s Vehicle
Licensing Statistics Database, which provides official records of all the vehicle
registrations across the United Kingdom. From this database, two specific datasets
were downloaded: VEH0171 and VEH0181.
VEH0171 contains detailed information about the number of new vehicle registrations
by manufacture and its model, while VEH0181 provides the information of the car
