Compare new data set to previously generated model?Hi all,
Question regarding how to compare a data set to the output of a previously generated response surface model. I have a response surface model that I developed based on a primary data set ("Data set A") with a response variable and three input variables. The input variables are environmental conditions - temperature, humidity, and light level, across specific ranges (e.g. 10-30C, 10-90%RH). I also have a second data set ("Data set B") that has the same response variable, obtained at a single temperature, RH, and light level. I want to test whether the response surface model's estimate of the response variable for the conditions used to generate Data set B is statistically different from the actual mean value of Data Set B. What would be the best way to do this? I thought about doing a one-sample t-test to compare Data Set B to the predicted value from the model, but that does not take into account the error associated with the model estimate.
Thanks for any suggestions.
OtherRe: Compare new data set to previously generated model?You have a single observation to test the null hypothesis, so you can't use a t-test.
You might instead use a prediction interval to decide if the new observation is unexpected.
Re: Compare new data set to previously generated model?@markbaileyThanks. Is there a way to view the prediction intervals from the model output window?
