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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
Constant (β 0) β 1 X 1 β 2 X 2 β 3 X 3 β 4 X 4 β 5 X 5
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
  1. Y = β 0 + β 1 X 1 + β 2 X 2 + … + β k X k
  2. β 0 = constant, β 1, 2,…, k  = regression coefficient
  3. X 1, 2,…, k  = independent variables
  4. Assumption for dummy variables in the equation
  5. Sex: Boy = 1, Girl = 0
  6. Treatment with headgear: tx1 = 1, tx2 = 0
  7. Treatment with Class II traction: tx1 = 0, tx2 = 1
  8. Treatment with extraction of four premolars: tx1 = 0, tx2 = 0