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Table 4 Significant variables in the models with linear, gamma, quantile, binary and multinomial logistic regression

From: Factors associated with overweight: are the conclusions influenced by choice of the regression method?

Variable Gamma Linear Quantile Binary logistic Multinomial logistic
0.25 0.50 0.75 Overweight Obesity
Age + + + + + + + +
Marital statusa
 With partner + + + + + + [+] +
Race/colourb
 Brown - - [-] - - - - -
 White -- -- -- -- -- -- -- --
Mother’s schoolingc
 9–11 years [+] [+] [-] 0 [+] [+] [+] [+]
 12 or more years ++ ++ + 0 ++ ++ [++] ++
Domestic overloadd
 High + + + + + + + +
Years worked at night + + 0 0 + 0 0 0
Self-rated healthe
 Poor + + + + + + + +
Consumption of fried foodf
 1–3×/month + + +++ + + ++ ++ +
 1–3×/week ++ ++ ++ ++ ++ + [+] +++
 4–6×/week +++ +++ ++++ ++++ +++ ++++ ++++ ++++
 Daily ++++ ++++ + +++ ++++ +++ +++ ++
Physical inactivityg
 Yes + + + + + + + +
BMI at age 20 years + + + + + + + +
  1. Reference categories: a “Without partner”; b “Black”; c “0–8 years”; d “Low”; e “Good”; f “Never or less than 1×/month”; g “No”
  2. The symbols “+” and “-” denote the direction of the association (“+” direct association, and “-” inverse association). The quantity of these symbols indicate the strength of the association (i.e., “++” indicate a stronger association than “+”, and “+++” indicate a stronger association than “++”). The symbol “0” indicate non-significant variables, and the symbol “[]” indicate only non-significant categories