A normal probability plot is a graphical technique used to determine if a dataset is approximately normally distributed. This plot is a special case of the Q-Q (Quantile-to-Quantile) plot, where the data is plotted against a theoretical normal distribution. If the data is normally distributed, the points on the plot will form an approximate straight line.

Key Principles

In a normal probability plot, the sorted data is plotted against values that are chosen to make the resulting image look close to a straight line if the data is approximately normally distributed. Deviations from this straight line suggest departures from normality, such as outliers, skewness, or kurtosis.

Axes of the Plot

• Horizontal Axis: Normal-order statistic medians.

• Vertical Axis: Order response values.

The order response variable can be calculated using the formula: [ N = G(U_i) ] where ( U_i ) is the uniform order median statistics and ( G ) is the percent point function of the normal distribution.