There are two different types of data that data is grouped into. It is important to be able to identify these differences as different techniques that you learn throughout Further Mathematics will be applied to either type of group.
Data that contains one variable is called univariate data, whereas data that contains two variables is called bivariate data.
Numerical data is collected of a quantity that is either measured or counted. For example, the different heights in a classroom are: 156cm, 167cm, 168cm, 155cm. Or the weight of various fruit at a shop: 4kg, 3.2kg, 4.2kg. In both examples there is a range of values being collected, thus they are numerical.
There are also two groups of data within numerical data and they separated into discrete and continuous numerical data.
Discrete data are numerical values which have a fixed value. Such as the number of cars that drive past a stop sign e.g. 1,2,3,4… These data values usually arise when we ask “how many”?
Continuous data usually arise when we ask “how much”? These numerical values come in a range of values that don’t necessarily need to have a whole number value. Examples of continuous data variables include the heights of students in a class. Since you can have students of 165.4cm and 176.3cm.
Most importantly, numerical data values have one set variable which a range of data values for that single variable. This distinguishes them from categorical data.
Categorical data is where data is quantified by specific groups of variables such as eye colour: blue, red, green. Even though that the value of each group of variable has a number assigned to it, what distinguishes categorical data from numerical data is that there a categories of values rather than a range of values.
There are also two different types of Categorical Datas that are separated into Nominal and Ordinal Data.
Nominal Data has no specific order in the categories. Such as eye colour there is no special significance of have red eyes before blue eyes.
Ordinal Data has a specific order of categories such as number of students achieving scores in the ranges of scores in a test: 90-100 (A+) 80-90 (A) 75-80 (B+) 70-75 (B) etc… Here the order of the categories is very important.
Note: It can sometimes be very confusing figuring out whether a set of data is categorical or numerical. It is important not to associate the type of data by the type of variable such as weight is purely numerical. It depends on how the variable is measured which determines what type of data is recorded. Such the weight of elephants at a zoo is a numerical data set whereas the weight of dogs at a vet are either 1 – underweight 2 – good weight or 3 – overweight which classifies weight into categories.
Want to suggest an edit? Have some questions? General comments? Let us know how we can make this resource more useful to you.