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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.

Choose Curves:Test for Distribution. |

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

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.

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. |

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