Predict after using Box Cox Transformation

I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 if it is not.

I did the Multiple linear regression on my data and found that it had non constant variance so I used Box Cox transformation.

The Box Cox transformation seemed to have worked very well. It had good residual vs. fitted values plots, residuals with a normal distibution and good r-squared and adjusted r-squared values.

The data I did the Box Cox transformation on was a training set. I now need to perform a model validation on the test set. I am using R to do my calculations. When I use the predict function in R the predicted values will be in the transformed state.

I would also like to use the cv.lm function in R which performs a cross validation using a given model and a data set. When I used this I am not quite sure which data set to use. The original or the transformed. Information on cv.lm can be found here http://www.statmethods.net/stats/regression.html and http://www.inside-r.org/packages/cran/DAAG/docs/CVlm

My questions are:

1. Once I have the predicted values can I just use the inverse of the Box Cox to get my values back to original?

2. If not how do I proceed from here to make sense of my model? I have looked a lot of places online and would really like some insight or expertise in this.

The most common use of the Box-Cox transformation is to make the residuals "better behaved"; that is, iid Normal(0, $\sigma^2 I$). If the residuals conform to this assumption after the transformation then the hypothesis tests (namely the F-test and t-tests) that one might like to perform to assess the significance of the estimated regression parameters are valid. To be clear, without the iid Normal(0, $\sigma^2 I$) assumption, the hypothesis tests are invalid. This is what I mean by inference.