Minitab automatically stores the last model that you fit for many of the
linear model analyses. You can use these models to quickly and
easily create graphs, make predictions, and optimize responses.
Minitab automatically saves the following types of models:

- Analyze Definitive Screening Design
- Analyze Binary Response for Definitive Screening Design
- Analyze Factorial Design
- Analyze Binary Response for Factorial Design
- Analyze Variability
- Analyze Response Surface Design
- Analyze Binary Response for Response Surface Design
- Analyze Mixture Design
- Fit General Linear Model
- Fit Mixed Effects Model
- Fit Regression Model
- Fit Binary Logistic Model
- Fit Poisson Model

After Minitab stores a model, you can use the following analyses. These analyses become active when the active worksheet contains a stored model that satisfies the minimum requirements.

Analysis | Purpose | Minimum requirements |
---|---|---|

Predict | To predict the value of the response variable (fitted values) for the combinations of variable settings that you request | Any number and type of variables |

Factorial Plots | To visualize the main effects and interaction effects | Any number and type of variables |

Contour Plot | To show how the fitted response relates to two continuous variables with a two-dimensional view | Two or more continuous variables* |

Surface Plot | To show how the fitted response relates to two continuous variables with a three-dimensional surface | Two or more continuous variables* |

Cube Plot | To see the factors and combination of settings used in your design | A 2-level factorial design |

Overlaid Contour Plot | To display contour plots for multiple responses in a single graph to visually identify an area where all of the responses are in a desirable range | Two or more continuous variables* |

Response Optimizer | To identify the combination of input variable settings that jointly optimize a single response or a set of responses | Any number and type of variables* |

Comparisons | To examine which factor level means are significantly different and to estimate by how much they are different. | You must have a general linear model or a mixed effects model |

*Mixed effects models do not produce contour plots, surface plots, cube plots, overlaid contour plots, or response optimizer plots.

After you fit a model for a response variable, an indicator appears in the bottom right corner of the column ID cell on the worksheet. A green indicator tells you that the model is up-to-date while a red indicator signifies an out-of-date model. A model is out-of-date when the data have changed since you fit the model. If you hover the pointer over the indicator, a tool tip appears that provides information about the model.

Each model-based analysis has a View Model button in the main dialog box that you can click before you use the analysis. You can examine all of the models of the same type that are stored in the worksheet. You can only use an analysis if the model is both up-to-date and satisfies the minimum requirements.

Double-click the graph. Then, right-click the graph and choose View Model Used for Graph from the context menu to see which model created the graph. If a graph is out-of-date, it could be due to changes in the model, data, or worksheet since you created the graph.