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Questions and Answers
What characterizes a symmetric binary variable?
Which of the following is true regarding ordinal data?
What is an example of an asymmetric binary variable?
Which scale classifies shirt sizes as {S, M, L, XL, XXL}?
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What operation can be performed on ordinal data?
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Which of the following best describes a nominal variable?
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What is an example of a binary variable?
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Why can't numerical values in nominal data be used for mathematical operations?
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Which statement is true about nominal data?
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What kind of scale is used to label data categories with a consistent naming convention?
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Which of the following is NOT an example of a nominal variable?
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How many categories does a binary variable have?
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Which of the following best exemplifies the nominal scale?
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What type of data is characterized by measurements that represent a meaningful order with no true zero point?
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Which scale of measurement allows for both ordering and meaningful differences, and contains a true zero value?
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Which type of dataset is primarily structured and typically found in relational databases?
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What type of data includes discrete categories without inherent order among them?
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In the context of data properties, which operation is primarily associated with numerical (quantitative) data?
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Which aspect categorizes data as either categorical (qualitative) or numeric (quantitative)?
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What characterizes the asymmetric binary type in the NOIR classification system?
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Which of the following is NOT a type of record data?
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What characterizes interval data compared to ratio data?
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Which of the following operations is NOT permissible on interval data?
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Which scale is used if there is a true zero and equal distances between values?
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Which of the following statements about discrete and continuous data is true?
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What can be transformed using affine transformations on interval data?
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Which of the following represents an ordinal scale?
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In which scale is it possible to perform negation on the values?
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How does a ratio scale differ from an interval scale?
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Study Notes
Types of Datasets
-
Record Data
- Relational records: Highly structured, often found in databases as tables.
- Data matrix: Numerical or cross-tabulated data.
- Transaction data: Records of events or transactions.
- Document data: Text documents represented as term-frequency vectors (matrices).
- Graphs and Networks
Data in Data Science
- Entity: A specific individual or object of interest.
- Attribute: A measurable or observable property of an entity.
- Data: A measurement or observation of an attribute.
Data Categorization
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NOIR Topology: A framework for classifying data types based on their properties:
- N: Nominal
- O: Ordinal
- I: Interval
- R: Ratio
Nominal Scale
- Definition: A variable with mutually exclusive categories that have no logical order.
-
Examples:
- Gender: {M, F} or {1, 0}
- Blood groups: {A, B, AB, O}
- Country codes: 048, 040
-
Note:
- Nominal data uses labels for categorization, which can be numbers, letters, or strings.
- Numerical values have no mathematical interpretation.
- Labels from different attributes can be combined to create new nominal variables.
- Examples: {A+, A-, AB+, etc.}
Binary Scale
- Definition: A nominal variable with exactly two mutually exclusive categories.
-
Examples:
- Switch: {ON, OFF}
- Attendance: {True, False}
- Entry: {Yes, No}
-
Note:
- A special case of nominal variables.
Symmetric and Asymmetric Binary Scale
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Symmetric: Both choices of a binary variable have equal importance.
- Example: Gender = {male, female}
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Asymmetric: Both choices of a binary variable have unequal importance.
- Example: Medical test (positive vs. negative)
- Convention: Assign 1 to the most important outcome.
Ordinal Scale
- Definition: Ordered nominal data, where categories have a logical order.
- Example: Shirt size = {S, M, L, XL, XXL}
-
Note:
- Can be compared using relational operators (<, ≤, >, ≥).
- Can be ranked.
- Numerical variables can be transformed into ordinal variables with a loss of information.
Interval Scale
- Definition: Data measured on a numerical scale with equal intervals between adjacent values, but no true zero.
-
Note:
- Interval data has well-defined intervals.
- 0 doesn't represent the absence of the attribute.
- Example: Temperature in Celsius and Fahrenheit.
Operation on Interval Data
- Addition and subtraction are possible.
- Negation and multiplication by a constant are permitted.
- Affine transformations are permissible (adding a constant or multiplying by a constant).
- One-to-one non-linear transformations (log, exp, sin, etc.) can be applied.
Continuous and Discrete Data
- Discrete data: Can only take on specific, individual values.
- Continuous data: Can take on any value within a certain range.
Ratio Scale
- Definition: Data measured on a numerical scale with equal intervals between adjacent values and a true zero.
-
Note:
- Ratio data can be in linear or non-linear scales.
- Operations like multiplication and division are meaningful.
- Example: Height, weight, age.
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Description
Explore the various types of datasets used in data science, including record data, graphs, and the NOIR topology for data categorization. This quiz covers fundamental concepts such as entities, attributes, and scales to help solidify your understanding of data classification.