Minitab 16 tutorials
![minitab 16 tutorials minitab 16 tutorials](https://img.directindustry.com/pdf/repository_di/13108/minitab16-whatsnew-469601_1mg.jpg)
If you are unsure of the parent distribution, or you know that the parent distribution is not in Minitab, then use the nonparametric method. Nonparametric method Parametric methods are not robust to severe departures from the distribution. Minitab includes a specific goodness-of-fit test with any tolerance interval so that you can assess the distribution. Use Tolerance intervals (Nonnormal distribution) if your data follow one of the following distributions: Use Tolerance intervals (Normal distribution) if your data follow a normal distribution. A goodness-of-fit test, such as the one that Minitab includes with Stat > Quality Tools > Individual Distribution Identification, can help you decide if your data follow a distribution. Use the parametric method if you know from prior experience or analysis that your population follows a known distribution. A parametric method allows you to achieve smaller margins of error with fewer observations, as long as the chosen distribution is appropriate for your data. Parametric method If your data follow a distribution, then a parametric method is more precise and economical than the nonparametric method. Use the intervals that match your situation, as follows: Minitab can calculate tolerance intervals using a parametric method, like the method that uses the normal distribution, or a nonparametric method.
![minitab 16 tutorials minitab 16 tutorials](https://i.ytimg.com/vi/CD810qFmJVg/sddefault.jpg)
For example, if the 95% tolerance interval for 99% of the population for the fill volume of 375 ml bottles is 358–381 ml, you can be 95% confident that 99% of the bottles to be filled in the future will have volumes that are within this interval. Tolerance interval A range of values for a product's characteristic that likely covers where a specified proportion of the population lies with a specified degree of confidence. For example, if the 95% PI of the average fill volume of 375 ml bottles is 360–379 ml, you can be 95% confident that the next sampled bottle will have a fill volume that is within this interval. Prediction interval A range of values for a product's characteristic that represents where the value of a single new observation is likely to fall with a specified degree of confidence. For example, if the 95% CI of the average fill volume of 375 ml bottles is 368–372 ml, you can be 95% confident that the true value of the process mean is within this interval. Confidence intervals (CI), prediction intervals (PI) and tolerance intervals are commonly used intervals derived from sample statistics.Ĭonfidence interval A range of values that is likely to contain the value of an unknown population parameter, such as the mean, with a specified degree of confidence.