Data Types and Classifications Quiz

Study Notes

Data is a collection of information, including numbers, words, measurements, observations, or descriptions.

Qualitative Data (Attribute Data): Describes characteristics or qualities. It is difficult to analyze numerically.
- Examples: Hair color, race, eye color, type of car driven.
Quantitative Data (Numerical Data): Results from counting or measuring.
- Examples: Number of people living in a place, basketball score, the number of books in a library.

Discrete Numerical Data: Represents counts.
- Examples: Number of people in a place, score in a game, book count in a library
Continuous Numerical Data: Represents measurements.
- Examples: IQ scores, basketball player height, volume of garbage production.

Nominal Level: Data consists of names, labels, or categories without order.
- Examples: Shirt colors
Ordinal Level: Data can be arranged in order, but differences between values are not meaningful.
- Examples: Rank order of winners
Interval Level: Ordered data with meaningful differences, but no natural zero starting point.
- Examples: Student IQ scores
Ratio Level: Same as interval, but with a natural zero point indicating none of the quantity exists.
- Examples: Employee income

The process of obtaining data for analysis is called sampling.
Random Sampling: Every member of the population has an equal chance of being selected.
- Example: Shuffling a deck of cards and randomly distributing them to players.
Systematic Sampling: Used when a population list is available; selects items at regular intervals.
- Example: To determine the nth item:
  - Number items from 1 to N (population size).
  - Decide on the desired sample size (m).
  - Calculate the interval (nth item) by dividing N by m.
  - Randomly choose a starting point within the interval and select every nth item.