Y based on the data on the left side of Figure 1. Real Statistics Data Analysis Tool: You can use Real Statistics’ Multiple Scatter Chart data analysis tool to create one or more scatter plots.Įxample 1: Create two scatter plots for X1 vs. You can build scatter diagrams in Excel as described in Excel Charts. 600) even when there is no linear association between the variables.įigure 2 – Non-linear association ( r =. This is generally so when the correlation coefficient is near zero.Īs can be seen in Figure 2, the correlation coefficient can be relatively high ( r =. In fact, in the latter case, it seems that the points are randomly scattered. 068, there is no apparent linear relationship between x and y. In the example with r = -.912, the linear correlation is also quite strong, but negative (note that the slope of the line that seems to fit the data is negative). This is not too surprising since r is almost at its maximum value of 1. 976 are very strongly positively correlated. Notice that the x and y values in the example with r =. This is done in Excel by highlighting the data in the two data sets and selecting Insert > Charts|Scatter.įigure 1 illustrates the relationship between a scatter diagram and the correlation coefficient (or covariance). You can assign different colors or markers to the levels of these variables.To better visualize the association between two data sets you can employ a chart called a scatter diagram (also called a scatter plot). You can use categorical or nominal variables to customize a scatter plot. Either way, you are simply naming the different groups of data. You can use the country abbreviation, or you can use numbers to code the country name. Country of residence is an example of a nominal variable. For example, in a survey where you are asked to give your opinion on a scale from “Strongly Disagree” to “Strongly Agree,” your responses are categorical.įor nominal data, the sample is also divided into groups but there is no particular order. With categorical data, the sample is divided into groups and the responses might have a defined order.

Scatter plots are not a good option for categorical or nominal data, since these data are measured on a scale with specific values. Some examples of continuous data are:Ĭategorical or nominal data: use bar charts Scatter plots make sense for continuous data since these data are measured on a scale with many possible values. Scatter plots and types of data Continuous data: appropriate for scatter plots Annotations explaining the colors and markers could further enhance the matrix.įor your data, you can use a scatter plot matrix to explore many variables at the same time. The colors reveal that all these points are from cars made in the US, while the markers reveal that the cars are either sporty, medium, or large. There are several points outside the ellipse at the right side of the scatter plot. From the density ellipse for the Displacement by Horsepower scatter plot, the reason for the possible outliers appear in the histogram for Displacement. In the Displacement by Horsepower plot, this point is highlighted in the middle of the density ellipse.īy deselecting the point, all points will appear with the same brightness, as shown in Figure 17. This point is also an outlier in some of the other scatter plots but not all of them. In Figure 16, the single blue circle that is an outlier in the Weight by Turning Circle scatter plot has been selected. It's possible to explore the points outside the circles to see if they are multivariate outliers. The red circles contain about 95% of the data. The scatter plot matrix in Figure 16 shows density ellipses in each individual scatter plot.

Scatter plot analysis examples