. 2020 Nov 4;63(5):249–253. doi: 10.1111/idj.12040

Table 2.

Summaries of curve estimation regression models

Model		Age						Hb						LD
		Parameter estimation				R²	P	Parameter estimation				R²	P	Parameter Estimation				R²	P
		Intercept	b₁	b₂	b₃	R²	P	Intercept	b₁	b₂	b₃	R²	P	Intercept	b₁	b₂	b₃	R²	P
Linear	Y = b₀ + (b₁ × t)	−1794.7	71.4			0.09	0.02	1631	−49.6			0.03	0.16	1602.8	−0.6			0.01	0.56
Logarithmic	Y = b₀ + (b₁ × ln(t))	−10081	3037.2			0.09	0.02	1453.3	−191.2			0.02	0.26	1318.3	12.7			0	0.97
Inverse	Y = b₀ + (b₁/t)	4132	−117106			0.09	0.02	1110.7	172			0.01	0.36	1586.7	−46570			0.01	0.56
Quadratic	Y = b₀ + (b₁ × t) + (b₂ × t²)	−5528.6	248.3	−2		0.1	0.05	1669.5	−76.5	1.1		0.03	0.37	1251.7	1	−0.0011		0.01	0.66
Cubic	Y = b₀ + (b₁ × t) + (b₂ × t²) + (b₃ × t³)	28252.4	−2277.7	58.6	−0.5	0.16	0.02	1552.2	70.7	−14	0.4	0.04	0.5	502.3	6.5	−0.0103	0.000004	0.03	0.65
Compound	ln(Y) = ln(b₀) + (ln(b₁)^t)	2	1.1			0.05	0.08	188.9	0.9			0.05	0.1	268.3	1			0.03	0.17
Power	ln(Y) = ln(b₀) + (b₁ × ln(t))	0.00003	4			0.06	0.06	127.8	−0.3			0.02	0.32	1181.7	−0.4			0.01	0.55
S-curve	ln(Y) = b₀ + (b1/t)	8.5	−159			0.06	0.07	4.3	0.3			0.01	0.4	4.9	−39.3			0	0.77
Growth	ln(Y) = b₀ + (b₁ × t)	0.7	0.1			0.05	0.08	5.2	−0.1			0.05	0.1	5.6	0			0.03	0.17
Exponential	ln(Y) = ln(b₀) + (b₁ × t)	2	0.1			0.05	0.08	188.9	−0.1			0.05	0.1	268.3	0			0.03	0.17
Logistic	ln(1/y–1/u) = ln (b₀) + (ln(b₁) × t)	0.9	0.9			0.06	0.05	0.004	1.1			0.05	0.09	0.003	1			0.03	0.21