ENGINEERING DATA ANALYSIS LESSON 1 TYPES OF DATA
Summary
TLDRThis video script delves into the realm of statistics, tracing its origins to 1749 and highlighting its evolution from state information to a broader analytical discipline. It defines statistics as the science and art of handling numerical data, encompassing collection, presentation, analysis, and interpretation. The script categorizes data into quantitative and qualitative types, further dividing them into continuous, discrete, attribute, and open data, with nominal and ordinal as subsets of attribute data. The importance of understanding these data types for research and analysis is emphasized.
Takeaways
- 📚 The history of statistics can be traced back to 1749, with the original meaning referring to information about states, and it has evolved to include both the collection and analysis of data.
- 🔍 Statistics is defined as the science and art of dealing with figures and facts, encompassing the collection, presentation, analysis, and interpretation of numerical data from various sources.
- 📈 Statistical data is a sequence of observations made on a sample from a population, and can be either quantitative or qualitative, serving as the foundation for further measurement and analysis.
- 📊 Quantitative data is measured by values or counts and is expressed as numbers, while qualitative data is non-numerical and characterized by descriptions or observations.
- 📉 Continuous data, also known as variable data, can take the form of decimals or continuous values, such as height measurements that can have infinite precision.
- 🏠 Discrete data, on the other hand, is data that is represented as whole numbers and cannot be divided into smaller units, like family size or class enrollment.
- 👕 Attribute data is countable and can be recorded for analysis, such as the size of a t-shirt, categorized as small, medium, large, etc.
- 🌐 Open data is qualitative and allows for a wide range of responses without a specific set of values, such as personal opinions or predictions about the future.
- 👥 Nominal data is used to categorize without implying an order, such as gender, race, or political affiliation, where the categories are distinct and not ranked.
- 🏅 Ordinal data indicates a ranking or order among categories, such as levels of awareness or skill levels, where there is a clear hierarchy.
- 📝 The process of statistics involves the collection of data, followed by its representation, analysis, and interpretation, which are key steps in understanding and applying statistical concepts.
Q & A
When is the history of statistics said to have started?
-The history of statistics is said to have started around 1749.
What does the original meaning of 'statistics' refer to?
-In early times, the original meaning of 'statistics' was restricted to information about the states.
How has the meaning of 'statistics' evolved over time?
-Over time, the meaning of 'statistics' has evolved to include not only the collection of information about states but also analytical work requiring statistical data.
What is the formal definition of statistics?
-The formal definition of statistics is the science and art of dealing with figures and facts.
What are the four main components of the process of statistics?
-The four main components of the process of statistics are the collection, representation, analysis, and interpretation of numerical data.
What is considered as statistical data?
-Statistical data are a sequence of observations made on a set of objects included in the sample drawn from a population, or it can be defined as quantitative or qualitative values of a variable.
What are the two main classifications of data?
-The two main classifications of data are quantitative and qualitative.
How is quantitative data defined?
-Quantitative data is defined as data that measures values or counts and is expressed as numbers.
What is the difference between continuous and discrete data?
-Continuous data, also known as variable data, can take the form of decimals or continuous values of varying degrees of precision. Discrete data, on the other hand, cannot be represented as decimals and are represented as whole numbers.
What are the two types of qualitative data?
-The two types of qualitative data are attribute data and open data. Attribute data can be counted for recording and analysis, while open data is not given a specific value and can have a wide range of responses.
How are attribute data further subdivided?
-Attribute data is further subdivided into nominal and ordinal data. Nominal data allows making statements only of quality or difference, while ordinal data has a rank or order.
Outlines
📊 Introduction to Data Collection and Statistics
This paragraph introduces the video's focus on data collection, explaining the types of data, methods of collection, and a brief history and definition of statistics. It mentions that the history of statistics dates back to 1749, with changes in the interpretation over time. Statistics is defined as the science and art of dealing with figures and facts, involving the collection, presentation, analysis, and interpretation of numerical data from various sources.
🔢 Understanding Quantitative and Qualitative Data
The second paragraph delves into the classification of data, distinguishing between quantitative and qualitative data. Quantitative data is measured by values or counts and is represented numerically, such as continuous data that can take decimal values like height, and discrete data that is represented as whole numbers like family size. Qualitative data, on the other hand, is non-numerical and describes or characterizes data, collected through observation and interviews. It is further divided into attribute data, which can be counted and analyzed, and open data, which allows for a wide range of responses without specific values.
📚 Further Classification of Qualitative Data
The final paragraph further breaks down qualitative data into nominal and ordinal categories. Nominal data allows for statements of quality or difference without an inherent order, such as gender, race, or political affiliation. Ordinal data, however, implies a ranking or order, like awareness levels or skill proficiency. The paragraph emphasizes the importance of understanding these data types for proper analysis and interpretation in research.
Mindmap
Keywords
💡Data Collection
💡Statistics
💡Quantitative Data
💡Qualitative Data
💡Continuous Data
💡Discrete Data
💡Attribute Data
💡Open Data
💡Nominal Data
💡Ordinal Data
Highlights
The history of statistics begins around 1749, evolving from state information to a broader analytical field.
Statistics is defined as the science and art of dealing with figures and facts, encompassing collection, presentation, analysis, and interpretation of numerical data.
The term 'statistics' originally referred to information about states, leading to the term 'easter egg statistics'.
Statistical data is a sequence of observations made on a sample drawn from a population, or defined as quantitative or qualitative values of a variable.
Data types are classified based on collection methods into quantitative and qualitative data.
Quantitative data measures values or counts and is expressed as numbers.
Qualitative data is non-numerical, characterized by observation or description, and collected through interviews and similar methods.
Quantitative data is further divided into continuous (variable data) and discrete (discontinuous data).
Continuous data can take the form of decimals or continuous values, such as height measurements.
Discrete data is represented as whole numbers, such as family size or enrollment numbers.
Qualitative data is divided into attribute data, which can be counted and recorded for analysis, and open data, which allows for a wide range of responses.
Attribute data includes nominal data, which allows for statements of quality or difference without order, such as gender or race.
Ordinal data is defined by an operation that allows for a rank order, such as awareness levels or skill rankings.
The importance of data in research studies is emphasized as the foundation for measurement and analysis.
The video will continue to discuss how data is presented after collection in subsequent content.
The video concludes with an invitation to enjoy learning more about statistics.
Transcripts
[Music]
hey
[Music]
okay so on this video this question we
are going to
discuss uh the uh
topic about data collections in which we
are going
to know what are the types of data
the methods of collecting data
and also we are going to take a little
review on the history and the definition
of
statistics
okay so we have the beginnings of
statistics so the history of
statistics can be said to start around
1749
although over time there have been
changes to the interpretation
of the world statistics in early times
the meaning was restricted to
information about the states in modern
terms
statistics means both sets of collected
information
as in national accounts and temperature
records and analytical work which
requires
statistical data so the beginnings of
statistics
is said to be around 1749 and the
previous uh meaning of the word is only
about the information about the states
so that's why we have the term easter
egg
statistics okay but later on
the uh name or the meaning of the
statistics uh go beyond the
uh collection of information about the
states but also the
analytical work which requires specific
statistical reference or the
interpretation of the information
that are collected from the different
states hence we have now
the word statistics
so the formal definition of statistics
is the science
and art of dealing with figures and
facts
so statistics is well defined as
collection presentation
analysis and interpretation of numerical
data collected from different sources
so is statistics
so statistics is a science
so that is and also an art so which
deals about
figures and facts
that is the most important terms in this
indus first definition
of the word statistics and also
so the the process of
uh statistics or the flow of statistics
starts from the collection of data
the representation analysis
and the interpretation so
that is how estatistics
works and that is mainly
one of the the concepts that we are
going to discover or
we are going to understand in our
subject
[Music]
okay so we have a statistical data so in
the previous
slides we learned the definition that uh
statistics is the science and art of
dealing with figures and data
and those figures and data can
can be put into one word or in this case
two words and that is statistical data
so a sequence of observation
made on a set of objects included in the
sample drawn from
our population so that is the first
definition of
statistical data it can be also defined
as quantitative or qualitative value of
a variable
so meaning could be number images words
figures
facts or ideas so it is the lowest unit
of
information from which other measurement
and analysis
can be done so that is also another
definition and data is one of the most
important and vital
aspects of any research
study so those are the definitions of
statistical data
then we have data types so data types
according to
the how they are collected so for
example
the raw data itself so they are
classified into different types
so if you are going to have a
3 3 diagram of that so we'll start with
data
so the very first two qualification
qualification
classification of data is quantitative
and qualitative so if we are going to
define what is quantitative data so it
is
data are measures of values or
counts and expressed as numbers so
meaning those data are expressed or
represented
as numbers so that is for quantitative
data
for qualitative data so defined as the
data that approximates or characterized
so meaning it only approximates
the data itself or also just
characterize or describe the data it is
non-numerical in nature
and collected through methods of
observation one-on-one interviews
and similar methods
then we can also divide the quantitative
data
into two groups so we have continuous
and discrete so the continuous data also
known as the variable data is data that
can
can take the form of decimals or
continuous
values of varying degrees of precision
so meaning the data can be represented
in
in numbers but they are we could
go to the realm of or to
we could express them as decimals so
that we could have
a degree of precision so meaning example
in that sample of a continuous data is
height
so we could not say that the height of a
person is exactly 120 centimeter
or equal or
just 130 centimeter 120 130 160 150
no every person has different height
so there are some exactly 120 but the
other one
might be have a height of 120.15
centimeter
so that is a type of a continuous data
also the other example for holy stick
that is the
weight here we will go to the script
so this script so these are data
whose form can take a uh
form or the form is cannot be
represented
as decimals or um they are just
represented as whole numbers so for
example family size enrollment size so
in a family size if you are going
if you will be asked the size of your
family so we see
six persons made up my families
so you could not say that six and one
half percent
or 6.5 percent so that is not
how family size is represented suppose
is enrollment size you could not say
that
uh in in a class there are 45.5 students
so it is always whole number and that is
considered as discrete
data or discontinuous data
then in the qualitative it is also
divided into two
so we have attribute and open
so we say attribute so data that can be
counted
for recording and analysis so it could
be counted for example
the uh the criteria for the height of a
person
tall
small so that is actually the data the
size of the waist so or the size of
the t-shirt of a person so we could have
small video large extra large global xl
triple xl
so that is an attribute data it can be
counted and can be also
recorded for analysis now the
opposite of this type is the open data
so the open data
is uh is depending on the sample and
that
are not given a specific value value
so it is not given and plus a specific
value the possible set of responses or
answers
so it is open free so
if you are going to give your response
so you could have
your response anything under the sun
so for example if you are going to have
a survey form
and you are asked that um
what uh how do you see
your life 50 years from now so that is
um considered to be as an open data
because
there are um maybe infinite
number or infinite uh types
of responses and that is considered as
open so the problem if this was
we could not analyze this properly
is sometimes um put in a survey form in
order
for the researcher to have a graph on
uh on the nature of the
uh of of the respondents
okay
then the up the attribute data is um
further subdivided into two so abnominal
and ordinal so the
nominal data is defined
by an operation which allows making
statements
only quality or difference so meaning
they are just criterias nominal data i
just created as for example
gender so gender male female race
american african european
religion so we have our roman catholic
islam cristo so on and so forth and have
political affiliation so if you are
a atmos if you are a democrat or a
republican if you are living in the
united states
so that is nominal data so there is no
order
only the criteria the quality of the
data
either it is uh either they are equal
or different okay
then we have ordinal so ordinal so we
could have the
um the data defined application whereby
the members are grouped
uh of a particular group are rough so
there is iran game for example awareness
so you have those people that are very
aware
not so aware i feel
then for example the
level of your dexterity so are you
um are you
very versatile not so versatile so
the the most common example of that is
for
for this uh for this generation is the
ranking on the ml so you can have the
mythic one meeting two
are you unknown so those ranking is
also an ordinal data so there is a wrap
there is
a order so that is ordinal
data so these are the types of data
the raw data that are collected
then after those data are collected we
could also
um group them
according to how they are now presented
in a certain way and that will be
discussed on
the next video so thank you for watching
and as always
enjoy learning
you
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