This study intends to better control the final composition of steel by trying to have a better knowledge of elements including copper, nickel, molybdenum, manganese, tin and chromium in the scrap. This objective was approached by applying probability and statistical concepts such as normal distribution, multiple linear regression and least square and non-negative least square concepts. The study was performed on the raw materials’ information of Ovako Smedjebacken and Ovako Hofors, two steel production plants in Sweden. The information included but were not limited to the amount of the different scrap types used in the charge, total weight of the charge and the final composition of the produced steel.  First, the concept of normal distribution was used as to consider the variations of the alloying elements between the estimated and measured alloy contents. The data were then used to consider a model for distribution factor of the studied elements. Also, an estimation of the alloy contents in the scrap type given the final steel composition was carried out using the concept of probability and statistics. At the end, a comparison of the results from the different concepts was done.