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**Statistical Thinking for Industrial Problem Solving**

In this video, we show how to check for normality using the **Distribution** platform and the file **Four Distributions**. You learn how to create and interpret a normal quantile plot, and how to fit a normal distribution to your data.

To start, we select **Distribution** from the **Analyze** menu.

We drag **Variable 1** and **Variable 2** to **Y, Columns**, and click **OK**. Then we select **Stack** from the top red triangle to change the results from a vertical to a horizontal layout.

Let’s look at the histogram and the box plot for **Variable 1**. The distribution appears to be approximately normal. The histogram is mounded in shape, and the tails are symmetric.

In the box plot, the mean and the median are close to one another, the median is close to the center of the box, and the whiskers are about the same length.

To create a normal quantile plot for **Variable 1**, we select the option from the red triangle next to **Variable 1**.

The points fall more or less on a diagonal line, with no unusual patterns. The distribution is approximately normal.

Let’s fit a normal curve to the data.

To do this, from the red triangle for **Variable 1**, we select **Continuous Fit** and then **Normal**.

The normal curve seems to fit the data well.

For comparison, we’ll repeat these steps for **Variable 2**.

From the histogram and box plot, you can see that the data are right-skewed.

You learn how to fit and compare different distributions in a future video.