Select the method or formula of your choice.

Each plotted point, *x*_{i}, represents the amount of time between successive events.

The center line is the 50^{th} percentile of the distribution.

Term | Description |
---|---|

Φ | cumulative distribution function of the standard normal distribution |

INV | inverse cumulative distribution function |

K_{z} | distance from the center line expressed in units of the standard deviation of a normal distribution; the value used for Test 1 |

The T chart analyzes the durations between subsequent events. If the data are entered as dates or times of events, then Minitab first converts the data to number of days between subsequent events. If all durations are greater than zero, then Minitab estimates the distribution parameters using the maximum likelihood method described in Parametric Distribution Analysis.

A duration of zero indicates that two events occurred simultaneously. If one or more durations is equal to zero, then Minitab uses an alternative method to estimate the parameters.

Let *y _{i}* = the duration (in days) between event

where Rank(*x _{i}*) is the rank, from smallest to largest, of

For each *y _{i}* = 0, exclude

__Weibull__

To estimate the parameters for the Weibull distribution, first transform both *y* and *x* by taking the natural log of each. Then use simple linear regression to fit the model *y* = *β*_{0} + *β*_{1}*x*. The scale for the Weibull distribution is estimated as exp(*β*_{0}) and the shape is estimated as 1 / *β*_{1}.

__Exponential__

The scale for the exponential distribution is estimated as the regression coefficient, *β*_{1}, in the simple linear regression equation, *y* = *β*_{1}*x*. Note that the intercept (*β*_{0}) is not fit in this model.