tool used to assess the correllation, rather than cause and effect relationship, between two variables eg. do icecream sales increase as the weather gets hotter

1. © ABCi 2019
What is a Scatter Plot?
A scatter plot is a mathematical diagram that plots variables that somehow “belong” together, against
each other, (e.g. age and height of a person under 18, outdoor temperature and ice-cream sales on a
given day, etc.).
What are the benefits of using this tool?
The human eye is fantastic at detecting patterns. Scatter plots enable you to visualise if there is a
relationship between variables and if so, the type of relationship e.g. linear or non-linear. The examples
given in this guide are linear in nature.
When would you use this tool?
Scatter plots help you determine if there is a relationship between two variables by plotting them
against each other on a graph, e.g. plotting age against height of a person under 18 years or
temperature against ice-cream sales.
How do you do it?
Let’s look at a specific example to create a scatter plot.
Hunch - there is a relationship between outdoor temperature
and ice-cream sales
Step 1: Collect data for the two variables (outdoor temperature
and ice-cream sales each day, one pair of observations per day)
– see table above right.
Step 2: Using Microsoft Excel or a piece of paper construct two
axis. One variable will be plotted along the
horizontal axis (temperature) and the other variable
along the vertical axis (ice-cream sales).
What resources do I need?
• You will need multiple sets of paired observations (measurements), meaning that each dot in the
scatter plot is made up of one observation for the first variable (e.g. age or ice-cream sales) plotted
against one observation for the second variable (e.g. height or outdoor temperature) in a given
circumstance (e.g. person under 18 or on a given day).
• Microsoft Excel, or if in doubt you can use graph paper and a pencil.
Average Daily
Temperature
(C)
Daily Ice-cream
sales
(£)
14.3 £214
16.5 £326
11.8 £184
15.1 £331
18.6 £407
22.2 £523
19.5 £411
25.0 £613
23.3 £543
18.2 £422
22.6 £446
17.3 £407
Scatter PlotABC
improvement
iSkills for

2. © ABCi 2019
How do you interpret a Scatter Plot?
The closer the dots are to making a line, the higher the correlation between the two variables (the
stronger the relationship), this may be positive or negative. The examples below demonstrate linear
(straight line) correlation.
In our example (above) there is a strong positive
correlation between outside temperature and ice-
cream sales, as the temperature rises, so do the ice-
cream sales.
Positive correlation – data points make an upwards
facing straight line
However if there is little or no correlation
between the two variables, there will not be a
visible line and the plots will be spread out
across the whole chart area – see left.
Hints & Tips
It is important to understand that correlation does not mean causation. Just because there is a
correlation between two variables, it does not mean that one variable has directly caused or affected
the other.
In our example (above) there is a strong negative
correlation between waiting time in clinic and
patient satisfaction score, as the waiting time
increases, the satisfaction score decreases.
Negative correlation – data points make a
downwards facing straight line
Hunch - there is a relationship between outdoor
temperature and ice-cream sales
Step 3: Plot each dot on your scatter plot by placing
the dot where the two variables meet. The first row
in the table has been plotted – see scatter plot right.
Step 4: Interpret the correlation of the two variables
by looking at the pattern of the dots on the graph.