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A **Q-Q plot**, short for “quantile-quantile” plot, is often used to assess whether or not a set of data potentially came from some theoretical distribution. In most cases, this type of plot is used to determine whether or not a set of data follows a normal distribution.

This tutorial explains how to create a Q-Q plot for a set of data in Excel.

**Example: Q-Q Plot in Excel**

Perform the follow steps to create a Q-Q plot for a set of data.

**Step 1: Enter and sort the data.**

Enter the following data into one column:

Note that this data is already sorted from smallest to largest. If your data is not already sorted, go to the **Data **tab along the top ribbon in Excel, then go to the **Sort & Filter **group, then click the **Sort A to Z **icon.

**Step 2: Find the rank of each data value.**

Next, use the following formula to calculate the rank of the first value:

**=RANK(A2, $A$2:$A$11, 1)**

Copy this formula down to all of the other cells in the column:

**Step 3: Find the percentile of each data value.**

Next, use the following formula to calculate the percentile of the first value:

**=(B2-0.5)/COUNT($B$2:$B$11)**

Copy this formula down to all of the other cells in the column:

**Step 4: Calculate the z-score for each data value.**

Use the following formula to calculate the z-score for the first data value:

**=NORM.S.INV(C2)**

Copy this formula down to all of the other cells in the column:

**Step 5: Create the Q-Q plot.**

Copy the original data from column A into column E, then highlight the data in columns D and E.

Along the top ribbon, go to **Insert**. Within the **Charts **group, choose **Insert Scatter (X, Y) **and click the option that says **Scatter**. This will produce the follow Q-Q plot:

Click the plus sign on the top right-hand corner of the graph and check the box next to **Trendline**. This will add the following line to the chart:

Feel free to add labels for the title and axes of the graph to make it more aesthetically pleasing:

The way to interpret a Q-Q plot is simple: if the data values fall along a roughly straight line at a 45-degree angle, then the data is normally distributed. We can see in our Q-Q plot above that the data values tend to deviate from the 45-degree line quite a bit, especially on the tail ends, which could be an indication that the data set is not normally distributed.

Although a Q-Q plot isn’t a formal statistical test, it offers an easy way to visually check whether or not a data set is normally distributed.