Statistical models are illustrated with a straight or curved line, depending on your selected chart statistic. The scatter plot indicates that most of the pipe surveys occurred in April.Ī scatter plot can use regression analysis to estimate the strength and direction of the relationship between dependent and independent variables. Using the Color by option, the department can style the points using unique colors for every unique value in the specified field. The public works department also wants to know whether there is any difference between pipes surveyed at different times of the year. A scatter plot can be used to plot the total number of leaks versus the total length of pipes in each zone. The department wants to know how much of an effect the total length of pipes has on the number of leaks compared to the impact of properties of the pipes, such as age or circumference. Two variablesĪ public works department has noticed an increase in leaks on water mains. The examples below show scatter plots using two variables, three variables, and bins. Scatter plots can answer questions about your data such as, What is the relationship between two variables? How is the data distributed? Where are the outliers? Examples The x-axis represents the independent variable, and the y-axis represents the dependent variable. You can assign different colors or markers to the levels of these variables.Scatter plots are used to determine the strength of a relationship between two numeric variables. 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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