This study was an examination of the usefulness of a statistical regression approach to identify prospective Engineering and Information and Computer Science (ICS) applicants most likely to succeed at the University of California at Irvine (UCI). The specific purpose was to determine the extent to which preadmissions measures such as high school grade point average (GPA) and admissions test scores could be used to predict college GPA. Multiple regression was used to maximize the correlation between the criterion variable (GPA) and predictor variables (preadmissions measures). Results indicate that cumulative GPA and major GPA of the presented samples of UCI Engineering and ICS majors can be reliably predicted using a linear combination of two preadmissions measures: high school GPA and the College Board Mathematics Achievement Test. None of the other three predictor variables (Scholastic Aptitude Test-Mathematics, Scholastic Aptitude Test-Verbal, and the College Board English Composition Achievement Test) added significantly to the predictions. Some shrinkage in the size of multiple correlations is expected when the equations of this study are applied to subsequent samples. However, in general, colleges that use the SAT and the high school record to predict freshmen GPA generally find that the results are fairly stable from year to year. (PN)