How to interpret a normal probability plot?

A normal probability plot is used to determine if a set of data follows a normal distribution.

A normal probability plot is a graph that compares the distribution of a set of data to a normal distribution. If the data follows a normal distribution, the plot will show a straight line. If the data does not follow a normal distribution, the plot will show a curve or other non-linear pattern.

To interpret a normal probability plot, first look at the shape of the plot. If the plot shows a straight line, the data is likely normally distributed. If the plot shows a curve or other non-linear pattern, the data is not normally distributed.

Next, look at the slope of the line. If the line is steep, the data is more spread out than a normal distribution. If the line is shallow, the data is less spread out than a normal distribution.

Finally, look at the position of the points on the plot. If the points are close to the line, the data is closely clustered around the mean. If the points are far from the line, the data is more spread out.

In summary, a normal probability plot is a useful tool for determining if a set of data follows a normal distribution. By examining the shape, slope, and position of the points on the plot, you can determine if the data is normally distributed or not.