If you read my blogs regularly, you’ll know that I’ve extensively used and written about linear models. We added a ton of features to Minitab that expand and enhance many types of linear models. I’m thrilled!
In this post, I want to share with my fellow analysts these linear model features and the benefits that they provide.
Poisson Regression: If you have a response variable that is a count, you need Poisson Regression! For example, use Poisson Regression to model the count of failures or defects.
Stability Studies: Analyze the stability of a product over time and determine its shelf life. We’ve even included a worksheet generator to create a data collection plan! For example, use Stability Studies to model drug effectiveness by batch across time.
The interface and functionality have also been both improved and standardized across many types of models. Previously, some features were only available for a specific type of linear model. For example, you could only perform stepwise regression in Regression, and you could only use the Response Optimizer in DOE.
We made significant improvements to the following types of linear models:
Thanks to the standardization across model types, all of the features I describe below apply to all of the above analyses! Pretty cool!
If your linear model has a lot of interactions and higher-order terms, you’ll love our new and improved interface for specifying the terms you need in your model. Additionally, you now have the ability to specify non-hierarchical models if you choose.
As an added convenience, we’ve also added the stepwise model selection procedure to all of the linear model analyses that I listed above. You also have greater control over how Minitab adds and removes terms from your model during this procedure.
It’s now a piece of cake to specify the best model for your data, but that’s often just the first step. If you need to use your model for additional tasks, you’ll come to love our stored models because they make it easy to perform important follow-up analyses!
In order to improve your workflow, we’ve introduced both the automatic ability to store models and a set of post-analyses to use with the stored models.
Every time you fit a model for one of analyses listed above, it gets stored right in the worksheet. After you settle on the perfect model, you can use the stored model to perform all of the post-analyses tasks below.
There’s a cool interface that makes it much easier to enter the values that you want to predict!
This main effects plot is based on continuous variables. Notice how it reflects the curvature in the model!
In this surface plot based on a binary logistic model, we see how students’ finances affect their probability of carrying a credit card.
Applications that involve multiple response variables present a different challenge than single response studies. Optimal variable values for one response may be far from optimal for another response. Overlaid contour plots allow you to visually identify an area of compromise among the various responses.
In this overlaid contour plot, the white region represents the combination of predictor values that yield satisfactory fitted values for both response variables.
This plot allows you to interactively change the input variable settings to perform sensitivity analyses and possibly improve upon the initial solution. The session window output contains more detailed information about the optimal settings and the predicted outcomes.