Version 12 of Design-Expert® is centered around the long awaited
Logistic Regression analysis option, which allows you to analyze data
that are binary in nature (true or false, zero or one, pass or fail).
In addition, we have added a few new general plotting tools, improved
the legend, and moved more graphs into a notebook (multi-pane) view.
- Responses with binary data (every cell is a 0 or 1) will have a Logistic Regression option on the Transform tab.
- Chi-squared tests for logistic regression model term significance.
- McFadden, Adj. McFadden, and Tjur pseudo-R-squared statistics are available for logistic regression.
- Binary responses can be optimized using criteria based on the probability of success or failure.
- Model Graphs have been moved to a notebook interface.
- You can create multiple graphs simultaneously in side by side views.
- All graphs now have a dockable legend that can be moved independently of the graph.
- The legend can also be docked to the right or bottom of the graph via the right-click menu.
- Elements in the legend can be toggled individually via the right-click menu.
- The font size of the legend can be increased or decreased by hovering over it, holding control, and using the mouse scroll wheel.
- A histogram of any column of data can be generated in the Graph Columns node.
- You can set a third axis on the Graph Columns scatterplot to make it into a 3d scatterplot.
- Effects graphs have been moved to a notebook interface and can be compared side by side.
- The Cube plot can now be toggled between observed and predicted values.
- Pairwise comparisons when clicking on a point in Interaction or One Factor plots are now available in a separate toolbox.
- Categoric variables can now use Treatment contrasts, which compares each level to a user-selected control, or reference level.
- Helmert contrasts are also available, which compares each level to the mean of the previous levels.
- Ordinal contrasts no longer need to be numeric. If they are not numeric they are assumed to be evenly-spaced (e.g. “Low”, “Medium”, “High”).
- The choice of contrast types for categoric factors is shown in the ANOVA.
- Kowalski-Cornell-Vining (KCV) models are available as a Model Order choice.
- KCV models provide a more efficient alternative to a full crossed model when building.
- KCV models can be selected during the analysis as well.