Bivariate data is a collection of two data sets, where each value in one set aligns with a value in the other. This is often gathered to explore the relationship between these variables and make informed decisions based on the findings. For example, data on outside temperature and ice cream sales or height and shoe size are both forms of bivariate data. The discovery of a correlation between temperature and ice cream sales could help businesses plan for hotter months by stocking up on ice cream.
The most commonly used method to represent bivariate data is through scatter graphs. This type of graph has two axes, with each variable plotted on a different axis. The data points are then marked on the graph and a regression line, also known as a line of best fit, can be drawn to determine the correlation between the variables. This line helps determine the direction of the relationship and how closely the data points align with it.
Step 1: Begin by drawing the axes and choosing an appropriate scale for the data.
Step 2: Label the x-axis with the independent variable (the one expected to change) and the y-axis with the dependent variable (the one likely to change due to the independent variable). Also, label the graph itself to describe its contents.
Step 3: Plot the data points on the graph.
Step 4: If necessary, draw a line of best fit.
As an example, let's look at the following data: the temperature on different days in July and the number of ice creams sold in a corner shop.
Temperature (°C)
14
16
15
16
23
12
21
22
Ice Cream Sales
16
18
19
43
12
24
26
For this data, temperature is the independent variable, so it should be plotted on the x-axis, and ice cream sales are the dependent variable, so they should be plotted on the y-axis. The resulting scatter graph would look like this:
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