# 2D Probability Plots

This type of graph is used to evaluate the normality of the distribution of a variable, that is, whether and to what extent the distribution of the variable follows the normal distribution. The selected variable will be plotted in a scatterplot against the values "expected from the normal distribution."

The standard normal probability plot is constructed as follows. First, the deviations from the mean (residuals) are rank ordered. From these ranks, STATISTICA computes z values (i.e., standardized values of the normal distribution) based on the assumption that the data come from a normal distribution (see Computation Note). These z values are plotted on the y-axis in the plot. If the observed residuals (plotted on the x-axis) are normally distributed, then all values should fall onto a straight line. If the residuals are not normally distributed, then they will deviate from the line. Outliers can also become evident in this plot. If there is a general lack of fit, and the data seem to form a clear pattern (e.g., an S shape) around the line, then the variable may have to be transformed in some way (e.g., a log transformation to "pull-in" the tail of the distribution, etc., see How to review and edit variable specifications). In addition to the Normal probability plot described above, Half-Normal and Detrended normal probability plots are also available. Half-Normal probability plots employ only the positive half of the normal curve in the analysis, and Detrended normal probability plots remove the linear trend of a normal probability plot to make lack-of-fit more apparent.