**software prediction confidance interval**.

That target parameter (X), for exemple mecanical resistence, is dependent of variables V1, V2 and V3. The software generates Xs, V1s, V2s and V3s. The software also gives the dimensions (in mm) of the product (P) components (C1, C2, C3). Posterior to the software estimation, 10 products, for example, are to be manufactured with the same especifications (iqual products) using the software given component dimensions. After they are manufactured, for each unit of the 10, X, V1, V2 and V3 are measured, obtaining Xm, V1m, V2m and V3m. There are a manufacturing error (e1) and known measurement error (e2) values.

There is also different types of components (C1a, C1b, C1c). So i also would have to estimate the software prediction confidance for different groups of products , depending on wich components they had, e.g., P1 : (C1a, C2a, C3b), P2 : (C1c, C2a, C3a) ... .

I have data of over a 200 products. For each product:

"P1, C1a, C2a, C3b, Xs, V1s, V2s, V3s, Xm, V1m, V2m, V3m"

**My question is**: How to best approach this problem? Do i simple estimate the difference in the measured and calculated mean values for each group, considering a t distribution? how to account for the manufactured error? Using regression analysis would be usefull?

Can i even utilize these statistical methods, since it is a

**non-problability sample**?

Any insight wold be of great help.

Thanks!