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Title: Factors Affecting Undergraduates’ Starting Salaries in the United States
Authors: 許, 家彥; 賴, 品心; 楊, 少閔; 楊, 東岳; 李, 惟中; Shu, Jar-Yuan; Lai, Pin Hsing; Yang, Shao-Min; Yang, Tung-Yueh; Li, Wei-Chung
Keywords: undergraduate starting salaries
USA
multiple regression analysis
model selection
diagnostic checking
residual analysis
Issue Date: 30-4-2018
Abstract: Abstract The purpose of this study is to explore what university-related factors affect undergraduate students’ starting salaries after their graduation in the United States. This study uses data of 102 U.S. universities retrieved from Kaggle.com and U.S. News & World Report. These sources provide information about eleven university-related factors: each school’s classification, year founded, average student-faculty ratio, tuition, total number of students enrolled, endowments received, location, acceptance rate, system of academic term, funding type (e.g., private/public), and ranking. The collected data is used to identify which factors have a significant correlation with undergraduates’ starting salaries. Multiple regression analysis, model selection, diagnostic checks, and tests for assumptions are applied to analyze the relationships between these 11 university-related factors (explanatory variables) and undergraduates’ starting salaries (response variable). The results demonstrate that 5 variables (classification, location, acceptance rate, funding type, and ranking of university) have a significant correlation with undergraduates’ starting salaries, while the other 6 variables do not. The study further finds that undergraduate students who graduate from private, top-ranked universities in the western region of the U.S. that primarily focus on engineering and have a low acceptance rate generally have higher starting salaries than students who graduate from other universities. This study identifies several factors for prospective undergraduates to consider when choosing U.S. universities that can yield higher salaries after graduation.
???metadata.dc.description.instructor???: 陳, 婉淑
???metadata.dc.description.course???: 數據分析
???metadata.dc.description.programme???: 商學大數據分析雙學士學位學程, 國際科技與管理學院
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