When creating a model, it can be very helpful to visualize both the data and the model. Often we wish to create a prediction model for a response variable on more than one predictors. In the case of a single response and two predictors, we must use a third dimension to visualize the the data and model.

In this app, you will be able to visualize the data and explore the effectiveness of different models for a numerical response variable.

The Response variable is:

The current model is:

The corresponding \(R^2-adjusted\) is:

The current model is:

The corresponding \(R^2-adjusted\) is:

When visualizing a categorical explanatory variable, we can utilize 2D plots instead. This is useful because it enables us to understand why the regression surfaces are seperate and gives us an expectation for what the regression surfaces will look like. Furthermore, 2D plot are by far, much easier to interpret.

The Response variable is:

The current model is:

The corresponding \(R^2-adjusted\) is:

The current model is:

The corresponding \(R^2-adjusted\) is: