Table 2:

Comparison of linear and quadratic models using error as the predictor

	Predictor	b	b 95% CI [LL, UL]	beta	beta 95% CI [LL, UL]	Model Fit	Difference between Models
WT	Linear Model
	Day Error	−0.21**	[−0.35, −0.06]	−0.46	[−0.77, −0.14]	R2 = 0.209** 95% CI[0.02,0.42]
	Quadratic Model
	Day Error	−0.53*	[−1.04, −0.01]	−1.15	[−2.28, −0.02]	R2 = 0.249*	ΔR2 = 0.040
	Day Error2	0.06	[−0.03, 0.16]	0.72	[−0.40, 1.85]	95% CI[0.02,0.44]	95% CI[−0.07, 0.15]
HET	Linear Model
	Day Error	−1.34*	[−2.53, −0.15]	−0.37	[−0.70, −0.04]	R2 = 0.138* 95% CI[0.00,0.35]
	Quadratic Model
	Day Error	−6.32**	[−10.27, −2.37]	−1.75	[−2.84, −0.65]	R2 = 0.295**	ΔR2 = 0.157*
	Day Error2	0.98*	[0.23, 1.72]	1.43	[0.34, 2.52]	95% CI[0.04,0.48]	95% CI[−0.05, 0.36]

Note. b represents unstandardized regression weights. beta indicates the standardized regression weights. LL and UL indicate the lower and upper limits of a confidence interval, respectively.

^*indicates p < 0.05.

^**indicates p < 0.01.