Probability Graph Paper Template
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- January 3, 2022 Updated
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Believe it or not; not all types of probability graphs are used to predict probability. A “normal” probability plot is most useful for interpreting data, understanding patterns in the data, and identifying data outliers by interpreting the plotted distribution patterns. In a nutshell - a normal probability graph will help you answer whether your data follows normal straight line distribution. In this template, we offer (normal) probability graph paper in portrait orientation and landscape orientation.
Here is a list of worksheets included in this template.
Probability Graph Portrait
Probability graph paper with labeled Y-axis, representing percent distribution of values, in portrait orientation.
Probability Graph Landscape
Probability graph paper with labeled Y-axis, representing percent distribution of values, in landscape orientation.
Using The Template
Personalize and Print
Add a personal touch! Each worksheet has a built-in header. Use the header to include information, such as name, title, and date.
Go ahead and hit print - each tab is configured to print in its appropriate orientation type.
How to Calculate Percent Distribution
In order to plot data points on probability graph paper we need to calculate percent distribution as it relates to the data sample.
First, all values in your dataset should be sorted in ascending order - these values will be plotted along the X-axis. Next, calculate percent distribution for each value using the formula below.
fi = (i-0.375)/(n+0.25)
i = the position of a value in the dataset
n = total number of values in the dataset
Once the percent distributions are calculated, each value can be plotted versus the percentage of values in the dataset, in linear fashion.
Below are good sources for more insight into using probability plots to understand your data.
The Minitab Blog - Bruno Scibilia explains how to analyze Lazer Tag data using probability plots.
Statistics by Jim - Jim Frost compares histograms to normal probability plots, and when to use them.
Working in Excel
Plotting Data Points
The probability graph template is ideal for printing and plotting by hand, but working in Excel is also an option. You can organize your data points and percent distribution in a table on a separate worksheet and plot the coordinates by inserting plot points in Excel.
Example. Organize data points in an Excel table or on a sheet of paper off-line.
To Insert plot points, go to the “Insert” menu and select “Illustrations”, then select “Shapes”, and choose a plot point icon, such as a circle.
Example. Annual precipitation pattern plotted in Excel using probability graph paper
A Quick Excel Chart
If you’re tired of manually inserting points and dragging them onto the graph paper template, there is a quick and easy way to produce a normal probability plot. All you need are your axes values.
To create a quick probability chart in Excel, organize your data (in ascending order) in an excel table. Next, highlight the dataset and select the “Insert” menu. Then, select the Scatter Chart (without lines) from the chart options to create a probability plot.
This is a quick way to answer the big question - “does my data follow a normal distribution?” In this example, you can quickly see that the data is somewhat normally distributed, but there are a few outliers because we cannot draw a straight line through all the data points.
Add Some Color
Background graph paper color can be jazzed up just the way you like it. To modify graph paper background, select the area and use the “Fill Color” option, under the “Home” menu to charge the color of the grid.
Grid lines can also be modified by using the “Border” tool to select a new “Line Color”.
The Y-axis is pre-labeled with a percent distribution scale, but the X-axis can be labeled to represent new, interesting data. To label the X-axis, manually enter a number scale along the bottom of the grid. Add an axis title by either inserting a textbox at the bottom or by typing a title directly into a cell.
We hope this article was helpful.
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