In the Fit Least Squares report, the Inverse Prediction option enables you to use a statistical model to infer the value of an explanatory variable, given a value of the response variable. Inverse prediction is sometimes referred to as calibration.
The Inverse Prediction option in the Estimates menu in Standard Least Squares also enables you to specify values for other explanatory variables in the model. The inverse prediction computation provides confidence limits for values of the explanatory variable that correspond to the specified response value. You can specify the response value to be the mean response or simply an individual response. For an example, see Example of Inverse Prediction.
When the model includes multiple explanatory variables, you can predict the value of X for the specified values of the other variables. You might want to predict the amount of running time that results in an oxygen uptake of 50 when someone’s resting pulse rate is 60. You might want separate inverse predictions for both males and females. Specify these requirements using the inverse prediction option.
The inverse prediction window shows the list of explanatory variables to the left. Each continuous variable is initially set to its mean. Each nominal or ordinal variable is set to its lowest level (in terms of value ordering). You must remove the value for the variable that you want to predict, setting it to missing. Also, you must specify the values of the other variables for which you want your inverse prediction to hold (if these differ from the default settings). In the list to the right in the window, you can supply one or more response values of interest. For an example, see Example of Inverse Prediction for Multiple Predictors.
Note: The confidence limits for inverse prediction can sometimes result in a one-sided or even an infinite interval. For technical details, see Inverse Prediction with Confidence Limits.