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Questions and Answers
What does NOIR stand for in data categorization?
What does NOIR stand for in data categorization?
- Nominal, Ordinal, Interval, Ratio (correct)
- Normal, Ordered, Interlinked, Ratio
- Numerical, Ordinal, Interval, Range
- None of the above
What type of data is considered quantitative?
What type of data is considered quantitative?
Numerical data
What is a nominal variable?
What is a nominal variable?
A variable that takes a value among a set of mutually exclusive codes with no logical order.
A nominal data variable can have mathematical interpretations.
A nominal data variable can have mathematical interpretations.
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What is an example of a binary variable?
What is an example of a binary variable?
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Which of the following is an example of nominal data?
Which of the following is an example of nominal data?
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The categorization of data types into NOIR allows for understanding the __________ of data.
The categorization of data types into NOIR allows for understanding the __________ of data.
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Study Notes
Datasets
- Record data
- Relational records: Database, relational tables, highly structured
- Data matrix: Numerical matrix, crosstabs
- Transaction data: Individual events
- Document data: Term-frequency vector (matrix) of text documents
- Graphs and networks
Data in Data Science
- Entity: A particular thing
- Attribute: A measurable property of an entity
- Data: A measurement of an attribute
Data Categorization - NOIR Topology
- N: Nominal
- O: Ordinal
- I: Interval
- R: Ratio
Nominal Scale
- Definition: Variables with mutually exclusive categories without a logical order.
- Examples:
- Gender: {M, F} or {1, 0}
- Blood groups: {A, B, AB, O}
- Rhesus (Rh) factors: {+, -}
- Country code: 048, 040
- Note: Data categorization using naming convention (numbers, letters, or strings). No mathematical interpretation of numerical values.
Binary Scale
- Definition: Nominal variable with two mutually exclusive categories without a logical order.
- Examples:
- Switch: {ON, OFF}
- True/False: {True, False}
- Yes/No: {Yes, No}
- Types:
- Symmetric: Both categories have equal importance.
- Asymmetric: One category is more important than the other.
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