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.
1.
Select Help > Sample Data Library and open Companies.jmp.
2.
Select Analyze > Fit Y by X.
3.
Select Sales ($M) and Y, Response.
4.
Select # Employ and X, Factor.
Fit Y by X Window
5.
Scatterplot of Sales ($M) versus # Employ
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
Place Your Mouse Pointer Over a Point
Example of a Scatterplot Matrix
This example uses the Solubility.jmp data table, which contains data for solubility measurements for 72 different solutes.
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.
Scatterplot Matrix
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
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.
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.
6.
From the red triangle menu, select Display Options > Box Plots.
Side-by-Side Box Plots
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.
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.
Overlay Plot of the Closing Price over Time
1.
3.
Select the Major Grid Lines check box.
4.
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.
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.
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.
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.
8.
Deselect Std Dev Chart on the red triangle menu.
Results Window
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.
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
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:
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.
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.
Initial Bubble Plot
The X and Y coordinates
2.
From the red triangle menu, select Trail Bubbles > Selected.
Japan’s History of Population Shifts