Examining Distributions |

You can add a graph to examine the cumulative distribution function, and you can test for distributions by using the Kolmogorov statistic.

Choose Curves:CDF Confidence Band:95%. |

**Figure 12.19:** Confidence Band Menu

This adds a graph of the cumulative distribution function with 95% confidence bands, as illustrated in Figure 12.20.

**Figure 12.20:** Cumulative Distribution Function

Choose Curves:Test for Distribution. |

This displays the test for distribution dialog. The default settings test whether the data are from a normal distribution.

**Figure 12.21:** Test for Distribution Dialog

Click OK in the dialog. |

This adds a curve to the graph and a **Test for Distribution** table to the window, as illustrated in Figure 12.22.

**Figure 12.22:** Test for Normal Distribution

The smooth curve in the graph represents the fitted normal distribution. It lies quite close to the irregular curve representing the empirical distribution function. The **Test for Distribution** table contains the mean (**Mean / Theta**) and standard deviation (**Sigma**) for the data along with the results of Kolmogorov's test for normality. This tests the null hypothesis that the data come from a normal distribution with unknown mean and variance. The *p*-value (**Prob > D**), also referred to as the *probability value* or *observed significance level*, is the probability of obtaining a *D* statistic greater than the computed *D* statistic when the null hypothesis is true. The smaller the *p*-value, the stronger the evidence against the null hypothesis. The computed *p*-value is large (**>0.15**), so there is no reason to conclude that these data are not normally distributed.

Related Reading |
Distributions, Chapter 38. |

Copyright © 2007 by SAS Institute Inc., Cary, NC, USA. All rights reserved.