Автор раздела: Danielle J. Navarro and David R. Foxcroft

Scatterplots

Scatterplots are a simple but effective tool for visualising the relationship between two variables, like we saw with the figures in the section on correlation (section Correlations). It is this latter application that we usually have in mind when we use the term “scatterplot”. In this kind of plot each observation corresponds to one dot. The horizontal location of the dot plots the value of the observation on one variable, and the vertical location displays its value on the other variable. In many situations you do not really have a clear opinions about what the causal relationship is (e.g., does A cause B, or does B cause A, or does some other variable C control both A and B). If that is the case, it does not really matter which variable you plot on the x-axis and which one you plot on the y-axis. However, in many situations you do have a pretty strong idea which variable you think is most likely to be causal, or at least you have some suspicions in that direction. If so, then it is conventional to plot the cause variable on the x-axis, and the effect variable on the y-axis. With that in mind, let us look at how to draw scatterplots in jamovi, using the same parenthood data set that I used when introducing correlations.

Suppose my goal is to draw a scatterplot displaying the relationship between the amount of sleep that I get (dani.sleep) and how grumpy I am the next day (dani.grump). There are two different ways in which we can use jamovi to get the plot that we are after. The first way is to use the Plot option under the Regression → Correlation Matrix button, giving us the output shown in Рис. 131. Note that jamovi draws a line through the points, we will come onto this a bit later in section What is a linear regression model?. Plotting a scatterplot in this way also allow you to specify Densities for variables and this option adds a density curve showing how the data in each variable is distributed.

Рис. 131 Scatterplot created with the Correlation Matrix analysis in jamovi

The second way do to it is to use the Exploration → Scatterplot function. This plot is a bit different than the first way, see Рис. 132, but the important information is the same.

Рис. 132 Scatterplot cretaed with the scatr add-on module in jamovi

More elaborate options

Often you will want to look at the relationships between several variables at once, using a scatterplot matrix (in jamovi via the Correlation Matrix → Plot command). Just add another variable, for example baby.sleep to the list of variables to be correlated, and jamovi will create a scatterplot matrix for you, just like the one in Рис. 133.

Рис. 133 Matrix of scatterplots cretaed with the Correlation Matrix analysis in jamovi.