Not directly. The ATP regression tool merely reformats and elaborates on the

output of LINEST. LINEST gives standard errors of regression coefficients,

which can be used to construct t-tests to test the significance of a given

coefficient, but which are useless for confidence intervals on predicted

values, because no native excel functions directly give the covariances

between these parameter estimates.

The Multiple Regression template by Business Spreadsheets adds a few things

beyond what LINEST gives, but standard errors and/or confidence intervals for

predicted values are not among them.

Excel's CONFIDENCE function has no bearing on your question. Moreover, it

is rarely useful for anything at all, because it presumes that you know the

true underlying standard deviation instead of having to estimate it from data.

If you are comfortable with linear algebra, you can "roll your own"

solution, however. If X is the model matrix (including a column of 1's for

the mean term) for the known y values, and v is the analogous row of

predictors for single point to be predicted, then if there are not numerical

issues, then

=MMULT(v,MMULT(MINVERSE(MMULT(TRANSPOSE(X),X)),MMULT(TRANSPOSE(X),y)))

should agree with

=TREND(y,X,v)

Excel versions since 2003 use a mathematically equivalent but numerically

superior calculation. If the two calculatiosn do not agree, the the

following confidence interval calculation will not be trustworthy. Assuming

that they agree, then

=SQRT(MMULT(MMULT(v,MINVERSE(MMULT(TRANSPOSE(X),X))),TRANSPOSE(v)))*S

(where S is the sey estimate from the LINEST output) is the standard error

of the fitted multiple regression at the predictors v. If you are predicting

a future value instead of just the fitted mean, use

=SQRT(1+MMULT(MMULT(v,MINVERSE(MMULT(TRANSPOSE(X),X))),TRANSPOSE(v)))*S

A 1-alpha level confidence interval is then

=prediction +/- TINV(alpha,df)*SE

so that alpha=0.05 would give a 95% confidence interval.

Jerry