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Table 2 Parameter estimates θ’s of the segmented regression model in Eq. 4

From: An analysis of Italian university students’ performance through segmented regression models: gender differences in STEM courses

Variable Category Estimate S.E. P value
Intercept   − 1.96 0.16 0.00
Gender f Male − 1.03 0.19 0.00
CU male   − 0.02 0.01 0.03
CU female   − 0.03 0.01 0.01
  Scientific − 0.15 0.06 0.02
  Classical − 0.17 0.07 0.02
HSdiploma (Other “liceo”) Technical − 0.39 0.08 0.00
  Vocational − 0.45 0.15 0.00
  Abroad/Other − 0.45 0.21 0.03
Age (≤ 19) > 19 − 0.53 0.04 0.00
  Biotechnology − 0.26 0.06 0.00
  Chemistry 0.03 0.06 0.69
  Computer Science − 0.49 0.06 0.00
Degree course (Biology) Engineering − 0.29 0.04 0.00
  Mathematics − 0.32 0.07 0.00
  Natural Sciences 0.08 0.06 0.20
  Physics − 0.28 0.07 0.00
  Statistics 0.12 0.10 0.23
  North 0.24 0.04 0.00
Macro-region of enrolment (Islands) Center 0.07 0.05 0.17
  South − 0.18 0.05 0.00
HS final mark (60)   0.02 0.00 0.00
  Scientific 0.28 0.14 0.05
  Classical 0.33 0.16 0.03
HSdiploma x Gender (Female) Technical 0.39 0.15 0.01
  Vocational 0.39 0.21 0.07
  Abroad/other 0.50 0.28 0.08
  1. Baselines are in brackets. Cohort of freshmen enrolled in 2014