Keywords

SAT, GPA

Abstract

This paper examines the course and national exam records for about fifty students in electrical engineering. The data includes the following: SAT/ACT scores, high school math and science grades, high school GPA, high school class rank, and grades for math, science, and engineering courses in the first two years of the major for undergraduates in electrical engineering. The data is analyzed seeking a correlation with success in the major which is measured by the cumulative GPA at or near graduation.

Two types of correlation data are presented. First, correlation with individual data items and success in the major is established. The results of this analysis shows that there are better items that can be used to predict success in EE than the results of standardized test scores. Second, linear combinations of various data items or formed and an optimal weighting sequences is established for prediction of success in the major.

The analysis may be useful to establish entrance requirements for high school students coming into the major and for consideration of upper level admission for those programs which further restrict entrance to the junior and senior level course work.

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Course and Exam Statistics in Electrical Engineering

This paper examines the course and national exam records for about fifty students in electrical engineering. The data includes the following: SAT/ACT scores, high school math and science grades, high school GPA, high school class rank, and grades for math, science, and engineering courses in the first two years of the major for undergraduates in electrical engineering. The data is analyzed seeking a correlation with success in the major which is measured by the cumulative GPA at or near graduation.

Two types of correlation data are presented. First, correlation with individual data items and success in the major is established. The results of this analysis shows that there are better items that can be used to predict success in EE than the results of standardized test scores. Second, linear combinations of various data items or formed and an optimal weighting sequences is established for prediction of success in the major.

The analysis may be useful to establish entrance requirements for high school students coming into the major and for consideration of upper level admission for those programs which further restrict entrance to the junior and senior level course work.