If the data clearly form a line or a curve, you may stop because variables are correlated. Look at the pattern of points to see if a relationship is obvious.(If two dots fall together, put them side by side, touching, so that you can see both.) For each pair of data, put a dot or a symbol where the x-axis value intersects the y-axis value. Draw a graph with the independent variable on the horizontal axis and the dependent variable on the vertical axis.Collect pairs of data where a relationship is suspected.When testing for autocorrelation before constructing a control chart.When determining whether two effects that appear to be related both occur with the same cause.After brainstorming causes and effects using a fishbone diagram to determine objectively whether a particular cause and effect are related.When trying to identify potential root causes of problems.When trying to determine whether the two variables are related, such as:.When your dependent variable may have multiple values for each value of your independent variable.This cause analysis tool is considered one of the seven basic quality tools. The better the correlation, the tighter the points will hug the line. If the variables are correlated, the points will fall along a line or curve. The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. You can assign different colors or markers to the levels of these variables.Quality Glossary Definition: Scatter diagram 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.
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