Scatterplots Use scatterplots to compare two continuous variables. Scatterplot Matrix Use scatterplot matrices to compare several pairs of continuous variables. Side-by-Side Box Plots Use side-by-side box plots to compare one continuous and one categorical variable. Overlay Plots Use overlay plots to compare one or more variables on the Y-axis to another variable on the X-axis. Overlay plots are especially useful if the X variable is a time variable, because you can compare how two or more variables change across time. Variability Chart Use variability charts to compare one continuous Y variable to one or more categorical X variables. Variability charts show differences in means and variability across several categorical X variables. Graph Builder Use Graph Builder to create and change graphs interactively. Bubble Plots Bubble plots are specialized scatterplots that use color and bubble sizes to represent up to five variables at once. If one of your variables is a time variable, you can animate the plot to see your other variables change through time.
JMP Learning Library – Graphical Displays and Summaries
Watch a brief video on data graphing techniques, including scatter plots, box plots, histograms and more.
The scatterplot is the simplest of all the multiple-variable graphs. Use scatterplots to determine the relationship between two continuous variables and to discover whether two continuous variables are correlated. Correlation indicates how closely two variables are related. When you have two variables that are highly correlated, one might influence the other. Or, both might be influenced by other variables in a similar way.
Example of a Scatterplot
This example uses the Companies.jmp data table, which contains sales figures and the number of employees of a group of companies.
 • What is the relationship between sales and the number of employees?
 • Does the amount of sales increase with the number of employees?
 • Can you predict average sales from the number of employees?
 1 Select Help > Sample Data Library and open Companies.jmp.
 2 Select Analyze > Fit Y by X.
 3 Select Sales (\$M) and Y, Response.
 4 Select # Employ and X, Factor.
Fit Y by X Window
 5 Click OK.
Scatterplot of Sales (\$M) versus # Employ
 1 Click on the point to select it.
 2 Select Rows > Hide and Exclude. The data point is hidden and no longer included in calculations.
 3 To re-create the plot without the outlier, select Script > Redo Analysis from the red triangle menu for Bivariate. You can close the original report window.
Scatterplot with the Outlier Removed
 • There is a relationship between the sales and the number of employees.
 • Sales do increase with the number of employees, and the relationship is linear.
 • You can predict average sales from the number of employees.
Place Your Mouse Pointer Over a Point
 • Click and drag with the mouse around the points. This selects points in a rectangular area.
 • Select the lasso tool, and then click and drag around multiple points. The lasso tool selects an irregularly shaped area.
Example of a Scatterplot Matrix
This example uses the Solubility.jmp data table, which contains data for solubility measurements for 72 different solutes.
 • Is there a relationship between any pair of chemicals? (There are six possible pairs.)
 • Which pair has the strongest relationship?
 1 Select Help > Sample Data Library and open Solubility.jmp.
 2 Select Graph > Scatterplot Matrix.
 3 Select Ether, Chloroform, Benzene, and Hexane, and click Y, Columns.
Scatterplot Matrix Window
 4 Click OK.
Scatterplot Matrix
 • All six pairs of variables are positively correlated.
 • The strongest relationship appears to be between Benzene and Chloroform.
The data points in the scatterplot for Benzene and Chloroform are the most tightly clustered along an imaginary line.
For example, if you select a point in the Benzene versus Chloroform scatterplot, the same point is selected in the other five plots.
Selected Points
 • the relationship between one continuous variable and one categorical variable
 • differences in the continuous variable across levels of the categorical variable
Example of Side-by-Side Box Plots
This example uses the Analgesics.jmp data table, which contains data on pain measurements taken on patients using three different drugs.
 • Are there differences in the average amount of pain control among the drugs?
 • Does the variability in the pain control given by each drug differ? A drug with high variability would not be as reliable as a drug with low variability.
 1 Select Help > Sample Data Library and open Analgesics.jmp.
 2 Select Analyze > Fit Y by X.
 3 Select pain and click Y, Response.
 4 Select drug and click X, Factor.
Fit Y by X Window
 5 Click OK.
 6 From the red triangle menu, select Display Options > Box Plots.
Side-by-Side Box Plots
 • The line through the box represents the median.
 • The middle half of the data is within the box.
 • The majority of the data falls between the ends of the whiskers.
 • A data point outside the whiskers might be an outlier.
 • There is evidence to believe that patients on drug A feel less pain, since the box plot for drug A is lower on the pain scale than the others.
 • Drug B appears to have higher variability than Drugs A and C, since the box plot is taller.
Example of an Overlay Plot
Note: To plot data over time, you can also use Graph Builder, bubble plots, control charts, and variability charts. For complete details about Graph Builder and bubble plots, see the Essential Graphing book. Refer to the Quality and Process Methods book for information about control charts and variability charts.
This example uses the Stock Prices.jmp data table, which contains data on the price of a stock over a three-month period.
 • Has the stock’s closing price changed over the past three months?
 • How do the stock’s high and low prices relate to each other?
 1 Select Help > Sample Data Library and open Stock Prices.jmp.
 2 Select Graph > Overlay Plot.
 3 Select Close and click Y.
 4 Select Date and click X.
Overlay Plot Window
 5 Click OK.
Overlay Plot of the Closing Price over Time
 1 From the red triangle menu, select Connect Thru Missing.
 2 Double-click the Y axis.
 3 Select the Major Grid Lines check box.
 4 Click OK.
Connected Points and Grid Lines
 1 Follow the steps in Creating the Overlay Plot of the Stock’s Price over Time, this time assigning both High and Low to the Y role.
 2
Two Y Variables
The legend at the bottom of the plot shows the colors and markers used for the High and Low variables in the graph. The overlay plot shows that the High price and Low price track each other very closely.
 • The first plot shows that the price of this stock has not remained the same, but has been decreasing.
 • The second plot shows that the high and low prices of this stock are not very different from each other. The stock price does not vary wildly on any given day.
Example of a Variability Chart
This example uses the Popcorn.jmp data table with data from a popcorn maker. The yield (the volume of popcorn for a given measure of kernels) was measured for each combination of popcorn style, batch size, and amount of oil used.
 • Which combination of factors results in the highest popcorn yield?
 1 Select Help > Sample Data Library and open Popcorn.jmp.
 2 Select Analyze > Quality and Process > Variability/Attribute Gauge Chart.
 3 Select yield and click Y, Response.
 4 Select popcorn and click X, Grouping.
 5 Select batch and click X, Grouping.
 6 Select oil amt and click X, Grouping.
Note: The order in which you assign the variables to the X, Grouping role is important, because the order in this window determines their nesting order in the variability chart.
Variability Chart Window
 7 Click OK.
 8 Deselect Std Dev Chart on the red triangle menu.
Results Window
 • The yield from small, plain batches is low.
 • The yield from large, gourmet batches is low.
 • Change variables by dragging and dropping them in and out of the graph.
 • Create a different type of graph with a few mouse clicks.
 • Partition the graph horizontally or vertically.
Example of a Graph That Was Created with Graph Builder
This example uses the Profit by Product.jmp data table, which contains profit data for multiple product lines.
 • How is the profitability different between product lines?
 1 Select Help > Sample Data Library and open Profit by Product.jmp.
 2 Select Graph > Graph Builder.
Graph Builder Workspace
 3 Click Quarter and then drag and drop it onto the X zone to assign Quarter as the X variable.
 4 Click Revenue, Product Cost, and Profit, and drag and drop them onto the Y zone to assign all three variables as Y variables.
After Adding Y and X Variables
 5 To change the box plots to a line plot, click the Line icon.
Line Plot
 6 To create a separate chart for each product, click Product Line, and drag and drop it into the Wrap zone.
Final Line Plots
Final Line Plots shows revenue, cost, and profit broken down by product line. The business analyst was interested in seeing the difference in profitability between product lines. The line plots in Final Line Plots can provide some answers, as follows:
 • Credit products, deposit products, and revolving credit products produce more revenue than fee-based products, third-party products, and other products.
 • However, the profits of all the product lines are similar.
 1 To remove Product Line from the graph, click the title of the graph (Product Line) and drag and drop it into any empty area within Graph Builder.
 2 To add Channel as the wrap variable, click Channel and drag and drop it into the Wrap zone.
Line Plots Showing Sales Channels
Line Plots Showing Sales Channels provides this answer: revenue and product cost for ATMs are the highest and are growing the most quickly.
Example of a Bubble Plot
This example uses the PopAgeGroup.jmp data table, which contains population statistics for 116 countries or territories between the years 1950 to 2004. Total population numbers are broken out by age group, and not every country has data for every year.
 • Is the age of the population of the world changing?
 1 Select Help > Sample Data Library and open PopAgeGroup.jmp.
 2 Select Graph > Bubble Plot.
 3 Select Portion60+ and click Y.
This corresponds to the Y variable on the bubble plot.
 4 Select Portion 0-19 and click X.
This corresponds to the X variable on the bubble plot.
 5 Select Country and click ID.
 6 Select Year and click Time.
 7 Select Pop and click Sizes.
 8 Select Region and click Coloring.
Bubble Plot Window
 9 Click OK.
Initial Bubble Plot
 • The X and Y coordinates
 • The bubble’s sizes
 • The bubble’s coloring
 • Bubble aggregation
 1 Click in the middle of the Japan bubble to select it.
 2 From the red triangle menu, select Trail Bubbles > Selected.
 3 Click the play button.
Japan’s History of Population Shifts
 • The proportion of the population 19 years old or less decreased.
 • The proportion of the population 60 years old or more increased.