What do cumulative distribution function plots in PROC UNIVARIATE superimpose for better analysis?

Cumulative distribution function (CDF) plots in PROC UNIVARIATE provide a visual representation of the probability distribution of a dataset. By superimposing probability distribution curves for various distributions, these plots allow analysts to compare the empirical distribution of their data against theoretical distributions, such as normal, exponential, or binomial distributions.

This comparison is crucial for assessing how well the chosen distribution fits the observed data. By visualizing multiple theoretical distribution curves over the CDF of the data, users can easily identify patterns, deviations, and potential outliers. The ability to see how well the empirical data aligns with different distributions supports more informed decisions regarding statistical modeling and hypothesis testing.

In contrast, other options like raw data points, confidence intervals, and regression lines do not aid in understanding the overall distribution of the data in the same manner. Raw data points show individual data values, confidence intervals provide an estimate of uncertainty around a parameter but do not focus on distribution, and regression lines illustrate relationships between variables rather than the distribution itself. Thus, superimposing probability distribution curves allows for a comprehensive analysis of the data's distributional characteristics.