Progress in Orthodontics

Table 2 Stepwise multiple regression models for soft tissue profile changes

From: Factors influencing soft tissue profile changes following orthodontic treatment in patients with Class II Division 1 malocclusion

Dependent variables (Y)	Prediction equation											R ²
Dependent variables (Y)	Constant (β ₀)	β ₁	X ₁	β ₂	X ₂	β ₃	X ₃	β ₄	X ₄	β ₅	X ₅	R ²
Pr (x)	10.3	2.20	Sex	−0.55	Age							0.315
Pr (y)	22.9	−0.22	SNB	−0.64	Age	0.06	NLA	0.94	Sex			0.290
Cm (x)	7.33	1.98	Sex	−0.37	Age							0.264
Cm (y)	22.3	−0.80	Age	1.26	Sex	−0.18	SNB	0.05	NLA			0.352
Sn (x)	5.48	1.69	Sex	−0.29	Age							0.210
Sn (y)	19.1	−0.68	Age	1.26	Sex	−0.15	SNB	0.05	NLA			0.339
Sls (y)	25.9	−0.73	Age	1.61	Sex	−0.17	SNB					0.327
Ls (y)	31.6	−0.86	Age	2.24	Sex	−0.21	SNB					0.376
Ss (x)	−2.18	0.13	tx1	1.85	tx2							0.099
Ss (y)	32.8	2.40	Sex	−0.78	Age	−0.24	SNB					0.356
Si (y)	8.93	0.06	NLA	−0.24	SNB	0.44	Lower lip thickness					0.176
Li (x)	19.5	−0.21	L1-NB angular	0.58	ANB	−0.48	Lower lip thickness	1.78	Sex	−0.07	NLA	0.403
Li (y)	17.1	0.47	Lower lip thickness	−0.66	Age	−0.17	L1-NB angular	0.08	NLA	−0.18	SNB	0.272
Ils (x)	16.0	−0.06	LMA	1.85	Sex	−0.08	NLA	0.32	ANB			0.332
Ils (y)	30.8	2.31	Sex	−0.82	Age	−0.19	L1-NB angular	0.07	NLA	−0.19	SNB	0.319
Pg’ (x)	13.1	2.28	Sex	−0.17	SN-GoGn	−0.46	Age					0.190
Pg’ (y)	9.02	2.28	Sex	−0.86	Age	0.09	NLA					0.151
Me’ (x)	8.25	−0.22	SN-GoGn	1.78	Sex	−1.22	tx1	−1.88	tx2			0.204
Me’ (y)	22.3	−1.16	Age	3.27	Sex							0.309

Y = β ₀ + β ₁ X ₁ + β ₂ X ₂ + … + β _k X _k
β ₀ = constant, β _{1, 2,…, k} = regression coefficient
X _{1, 2,…, k} = independent variables
Assumption for dummy variables in the equation
Sex: Boy = 1, Girl = 0
Treatment with headgear: tx1 = 1, tx2 = 0
Treatment with Class II traction: tx1 = 0, tx2 = 1
Treatment with extraction of four premolars: tx1 = 0, tx2 = 0

Back to article page