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The normal probability plot (also known as a QQ plot) is a specialized graph that helps you to decide whether using a normal model is appropriate. If the distribution of your data is normal, the data plots will go in an approximate diagonal line with no deviations. A normal probability plot compares each data value with what would be the data value's z-score if it were perfectly normal. A normal probability plot is placed on the x (Expected Normal Value) and y axis with plots lined in their appropriate position. The "perfect plot" will be shown with a diagonal line.
For Example: Data percentages were collected from various countries on the amounts of Cannabis. As you can see in the Q-Q Plot below, the data values keep in an approximate diagonal line, so you can continue with a Normal model.
How to Generate a Normal Probability Plot Using SPSS
In the menu bar, click Analyze.
Highlight "Descriptive Statistics" with your mouse and a side menu should appear.
In the side menu, open the Q-Q plot option.
On the left of the new window, you will find your quantitative variable you are wanting to test. Drag that variable to the "Variables" area.
Click OK.
In the Output window of your SPSS, your graph should appear. You need to scroll through your output data until you find the plot titled: "Normal Q-Q Plot of (Name of Data)".
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