1.4 Classifying and Organising Data

Data collected in original form is called raw data

Data can be classified into two types

  • Categorical data: Qualitative data, usually collected through observation and is descriptive. E.g. colour, texture, etc.
  • Numerical data: Quantitative data consisting of numerical values.

There are 2 types of numerical data:

  • Discreet data:
    • It is data based on counts, it is data that can be associated with a specific value.
    • Bar graph represents discreet data only
  • Continuous data:
    • Data representing series of values or values grouped into categories.
    • Histogram represents continuous data.

A frequency distribution is the organisation of raw data in table form, using classes and frequencies.

The data can be placed in categories and organized in categorical frequency distributions.

  • Example:
  1. A survey can be used to gather information about a group. Often, a part of the group, called a sample, is chosen to represent the whole group or population
  2. A sample must represent the population fairly. In a random sample, each person in the population has an equal chance of being chosen.
  3. A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter being estimated eg when certain individuals are being more likely to be selected than others.
  4. An estimator or decision rule with zero bias is called unbiased.
  5. Analysing information involves identifying and describing trends(patterns in data represented in tables/graphs and explain what the data indicates about the question/problem for which the data was collected.
Engage the videos below and respond to the popup questions
Video 1: Organizing Data



 

Video 2: Discrete and Continous data 

MooMooMath & Science. (2016). Discrete and continous data (Standard YouTube licence)

Video 3: Types of data: Categorical vs numerical

365 Data Science. (2019). Types of data: Categorical vs numerical (Standard YouTube licence)