Table 3.

Linear regression predicting blame across scenarios.

	Robot blame	Surgeon blame	Hospital blame	Other/No blame
Past Surgery	−.05 (−2.76)**	.01 (0.55)	.06 (2.87)**	−.01 (0.71)
Gender (0 = Male; 1 = Female)	−.01 (−0.56)	−.04 (−2.01)*	.01 (.056)	.07 (3.49)**
Age range	−.04 (−2.21)**	.01 (0.87)	−.03 (−1.75)	.09 (4.52)**
Education Level	−.07 (−2.82)**	.04 (1.63)	−.00 (−.07)	.04 (1.77)
Profession Business	−.00 (0.01)	.08 (3.39)**	−.03 (−1.38)	−.09 (−3.77)**
Profession Computing	−.04 (−1.65)	.05 (2.17)*	.01 (0.50)	−.04 (−1.82)
Profession Healthcare	−.09 (−3.49)**	.11 (4.17)**	−.04 (−1.57)	.01 (0.66)
Overall R	R = .16 F(7,2183) = 8.89**	R = .13 F(7,2183)= 6.15**	R = .08 F(7,2183) = 2.50*	R = .17 F(7,2183) = 9.67**

Regression predicting total blame distribution across 5 scenarios. Standardized regression beta values presented, t values presented in parentheses.

*p < .05, **p < .01.