![]() A negative correlation is the opposite – when one value increases, the other will decrease. A positive correlation is like what we have seen in our previous example – an increase in one amount will result in an increase in the other. We either have a positive, negative or zero correlation (this is also called ‘no correlation’). Correlations in scatter graphsĬorrelations on a graph can be one of three types. This is called a positive correlation as an increase in the x value on the graph gives an increase in the y. Looking at this we can see that the dots all tend to show a trend in the data – the higher someone scores in Maths, the higher they tend to score in Science. From this we can quickly see the scores of people by checking where each dot lies on the x and y axes. This allows us to see a series of dots that represent individual students. Doing this will require us to use a clear graph and accuracy in our drawing.įrom the above graph we can see that the scores of students in the Maths test have been plotted along the x, while Science scores are along the y. Then we can plot each individual student as required. Here we need to use Science and Maths marks as our x and y on a graph. Plot these on a scatter graph and comment on any links between the marks that students get in Maths compared to their mark in Science. The marks of 10 students in a Maths and Science exam were recorded in the table below with marks being out of 50: Let’s look at an example of how we might go about this below. ![]() In order to draw out a scatter graph you must plot both the x and y coordinates (much like in a graph) and then set each as a particular value. With this information we could look at two variables and see if there are any correlations between these.Ĭorrelation – the link between two different variables and how these relate to each other. ![]() ![]() This allows us to find trends in how data sets are linked. Scatter graphs are used to plot two different outcomes against each other. ![]()
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