Excel does not natively provide all the information necessary in a convenient

form. Therefore how much YOU understand of statistics is more to the point.

Absent information about the specific multiple regression model you want to

fit, I will have to assume that you understand basic linear algebra as well.

Sorry in advance if this is too heavy to slog through.

You will need to calculate the variance of an estimated point on the

multiple regression. Suppose X is the "design matrix" (the array that you

would pass to LINEST, augmented with a column of ones [unless you are not

fitting a constant term]). If b is the corresponding vector of coefficient

estimates (a column, and in reverse order to the LINEST output), then

=MMULT(X,b) gives the estimated multiple regression at your data points, i.e.

the same output as =TREND(known_y's,known_x's,,const). The predicted value

at a given point on the multiple regression would be =MMULT(v,b) where v is

the row of X corresponding to the point (if it is in the data set), or is

constructed similarly (if it is not in the data set). You will need D which

is calculated as

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

The variance of an estimated point on the multiple regression is then

=D*MSE, where MSE is =sey^2 and sey is one of the quantities output by

=LINEST(known_y's,known_x's,const,TRUE). Similarly, the variance of a

predicted future point that follows the same multiple regression is

=(1+D)*MSE.

A 95% 2-sided confidence interval for a point on the multiple regression

line is then

=MMULT(v,b) +/- SQRT(D*MSE)*TINV(0.05,df)

A 95% 2-sided prediction interval for a new point that follows the multiple

regression is

=MMULT(v,b) +/- SQRT((1+D)*MSE)*TINV(0.05,df)

Some simplification may be possible given knowledge of the particular

regression model that you want. For instance, with simple linear regression:

MSE reduces to STEYX(known_y's,known_x's)

=MMULT(v,b) reduces to

=FORECAST(x,known_y's,known_x's)

or equivalently to

=INTERCEPT(known_y's,known_x's) +x*SLOPE(known_y's,known_x's)

and D reduces to

=1/COUNT(known_x's) +(x-AVERAGE(known_x's))^2/DEVSQ(known_x's)

Jerry