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Questions and Answers
What distinguishes asymmetric binary variables from symmetric binary variables?
What distinguishes asymmetric binary variables from symmetric binary variables?
Which of the following is an example of an ordinal variable?
Which of the following is an example of an ordinal variable?
What operations can typically be performed on ordinal data?
What operations can typically be performed on ordinal data?
In a binary variable related to medical testing, what convention is often used?
In a binary variable related to medical testing, what convention is often used?
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What is one consequence of transforming a numerical variable into an ordinal variable?
What is one consequence of transforming a numerical variable into an ordinal variable?
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Which scale of measurement is used for variables that have a true zero point?
Which scale of measurement is used for variables that have a true zero point?
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What is a characteristic of nominal data?
What is a characteristic of nominal data?
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Which of the following datasets falls under relational records?
Which of the following datasets falls under relational records?
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Which type of data can be described with the operations of addition and subtraction?
Which type of data can be described with the operations of addition and subtraction?
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An example of a binary nominal scale is?
An example of a binary nominal scale is?
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Which data type is represented by a term-frequency vector of text documents?
Which data type is represented by a term-frequency vector of text documents?
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What distinguishes ordinal scales from nominal scales?
What distinguishes ordinal scales from nominal scales?
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Which of the following properties is NOT associated with quantitative data?
Which of the following properties is NOT associated with quantitative data?
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What is a characteristic of interval data?
What is a characteristic of interval data?
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Which of the following is an example of interval data?
Which of the following is an example of interval data?
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What operation can be performed on interval data?
What operation can be performed on interval data?
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Which scale of measurement has both equal intervals and a true zero?
Which scale of measurement has both equal intervals and a true zero?
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Discrete data is best described as data that can take:
Discrete data is best described as data that can take:
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Which type of data is classified as categorical (qualitative)?
Which type of data is classified as categorical (qualitative)?
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What defines the interval scale in data measurement?
What defines the interval scale in data measurement?
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In NOIR classification, which data type is considered numeric and continuous?
In NOIR classification, which data type is considered numeric and continuous?
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What characterizes a nominal variable?
What characterizes a nominal variable?
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Which of the following is an example of a binary variable?
Which of the following is an example of a binary variable?
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How many categories can a nominal variable have at minimum?
How many categories can a nominal variable have at minimum?
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What is the significance of numbers in nominal data?
What is the significance of numbers in nominal data?
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Which of the following is NOT a feature of a nominal scale?
Which of the following is NOT a feature of a nominal scale?
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What is an example of a nominal scale in everyday use?
What is an example of a nominal scale in everyday use?
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Which of the following best describes a ternary variable?
Which of the following best describes a ternary variable?
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Which of these statements about nominal and binary scales is accurate?
Which of these statements about nominal and binary scales is accurate?
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Study Notes
Data Types and Datasets
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Record Data: This type of dataset contains information organized as records, which can be relational, matrix, transaction, or document data.
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Relational Records: Data is structured in relational tables, common in databases.
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Data Matrix: Represents data in a numerical format or as a table of frequencies.
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Transaction Data: Contains records of purchases, events, or other transactions.
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Document Data: Text documents represented as term-frequency vectors (matrices) for analysis.
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Graphs and Networks: This dataset represents relationships between entities using nodes and edges.
Data in Data Science
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Entity: A specific item or object being analyzed.
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Attribute: Measurable property of an entity.
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Data (Measurement): Values obtained by measuring or observing attributes of an entity.
Data Categorization: NOIR Topology
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Nominal Scale: Categorizes data using labels with no inherent order. Examples include gender, blood groups, and country codes.
- Binary: A nominal scale with two categories (e.g., ON/OFF, True/False). Binary scales can be symmetric (both categories have equal importance) or asymmetric (one category is more significant than the other).
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Ordinal Scale: Categorizes data with a defined order between categories but without equal intervals. Examples include shirt sizes (S, M, L, XL), ranking positions, or levels of agreement (strongly disagree, disagree, ...).
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Interval Scale: Categorizes data with equal intervals between values but no true zero point. Examples include temperature scales (Celsius, Fahrenheit) or dates.
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Ratio Scale: Categorizes data with equal intervals and a true zero point. Examples include height, weight, or income.
Qualitative vs. Quantitative Data
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Qualitative data: Categorical data that provides descriptions and insights.
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Quantitative data: Numerical data that provides measurements and quantifiable information.
Properties of Data
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Distinctiveness: Determines if two data points are equal or not. This applies to Categorical (Qualitative) data.
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Order: Relates data points using greater than or less than operators. This applies to Categorical (Qualitative) data.
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Addition: Allows data points to be combined using addition or subtraction. This applies to Numerical (Quantitative) data.
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Multiplication: Allows data points to be combined using multiplication or division. This applies to Numerical (Quantitative) data.
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Description
Explore the various types of data and datasets essential in data science. This quiz covers record data, relational records, transaction data, and document data, alongside concepts like attributes and entities. Test your understanding and application of these foundational topics.