Qualitative vs Quantitative variables
Qualitative variables are categorical items, whereas quantitative variables have a numerical value associated.
Qualitative example: Blood group
Quantitative example: Temperature
Discrete vs Continuous variables
Quantitative variables can be split into discrete (counted to an exact figure) or continuous, which can’t be measured precisely so need to be rounded.
Discrete example: No of children in a family
Continuous example: A length measured to the nearest cm
A variable that represents a numerical value within a chance experiment. Discrete random variables can assume only a specific number of values at isolated points whereas continuous random variables can assume any value.
Class intervals can be used to categorise continuous quantitative variables e.g. lengths grouped at 10cm intervals 0-10cm, 10-20cm, etc. This effectively turns them into a qualitative variable, making them easier to summarise on your reports.
Primary data vs secondary data
Primary data is directly collected for the experiment, whereas secondary data comes from an external source.
Qualitative variables can be summarised by scales as well as class intervals.
Nominal scales are unordered scales where the category names follow no logical order e.g. gender:
Ordinal scales are scales with a logical order e.g. survey responses of:
- Strongly Agree
- Strongly Disagree
Univariate and bivariate data
Univariate data comes from one source only. Bivariate data is two different dependent variables from the same population. The goal of examining bivariate data is usually to show a relationship or association between the two variables and can be tracked effectively using scatter plot charts.
Univariate example: Attendance figures for a football team
Bivariate example: Attendance figures for a football team compared with matchday beer sale figures
The five-number summary of a variable consists of its minimum value, the first quartile (Q1), Q2, Q3 and its maximum value.