ENGINEERING DATA ANALYSIS LESSON 1 TYPES OF DATA

Summary

This 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

00:00

📊 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.

05:01

🔢 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.

10:02

📚 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.

Keywords

💡Data Collection

Data Collection refers to the process of gathering and compiling information from various sources for analysis. In the context of the video, it is the first step in the statistical process, where different types of data are collected using various methods. The video emphasizes the importance of data collection as the foundation for any statistical analysis, highlighting its role in understanding the subject matter.

💡Statistics

Statistics is defined in the video as the science and art of dealing with figures and facts. It encompasses the collection, presentation, analysis, and interpretation of numerical data from different sources. The term has evolved from its early meaning, which was restricted to information about states, to its modern interpretation that includes analytical work requiring statistical data. The video uses the history and formal definition of statistics to establish its significance in data analysis.

💡Quantitative Data

Quantitative data is characterized by measurements of values or counts that are expressed as numbers. The video script explains that this type of data can be represented as decimals or continuous values, such as height or weight, providing a degree of precision. Quantitative data is a key aspect of statistical analysis, as it allows for numerical comparisons and calculations.

💡Qualitative Data

Qualitative data, as described in the video, is non-numerical in nature and is collected through observation, interviews, and other methods that describe or characterize the data. This type of data is important for understanding the attributes or qualities of the subjects being studied, such as opinions, experiences, or descriptions.

💡Continuous Data

Continuous data, also known as variable data, can take the form of decimals or have continuous values with varying degrees of precision. The video provides the example of a person's height, which can be measured to a high degree of accuracy, such as 120.15 centimeters, illustrating the concept of continuous data in statistical analysis.

💡Discrete Data

Discrete data, in contrast to continuous data, is data that is represented as whole numbers and cannot be divided into smaller units. The video uses examples such as family size or enrollment size, where the number of individuals is counted as whole entities, and cannot be expressed as fractions or decimals.

💡Attribute Data

Attribute data is a type of qualitative data that can be counted and recorded for analysis. The video script mentions criteria such as the size of a person's waist or the size of a t-shirt, which can be categorized into small, medium, large, etc., and are used to classify and analyze data based on specific attributes.

💡Open Data

Open data is qualitative data that is not given a specific value and allows for a wide range of responses. The video provides the example of a survey question asking how one sees their life 50 years from now, which can elicit an infinite variety of responses, making it an example of open data.

💡Nominal Data

Nominal data is a subtype of attribute data that allows for statements of quality or difference but does not imply an order. The video uses examples such as gender, race, and political affiliation, which are categories that distinguish between different groups without a hierarchical order.

💡Ordinal Data

Ordinal data is another subtype of attribute data where the members of a group are ranked in a specific order. The video gives the example of ranking in a game or level of awareness, where there is a clear order from the least aware to the most aware, illustrating the concept of ordinal data in statistical analysis.

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