Type of Regression Model

Variables

Model No.

Model Equation

Goodness of Fit

Simple Linear Regression Model

y = a₀ + a₁x

CBR = f(PL)

1

CBR = 25.90 − 0.061*PL

R² = 0.009

MSE = 17.57

RMSE = 4.19

CBR = f(LL)

2

CBR = 23.99 + 0.005*LL

R² = 0.00

MSE = 17.73

RMSE = 4.21

CBR = f(PI)

3

CBR = 25.46 − 0.054*PI

R² = 0.01

MSE = 17.62

RMSE = 4.20

CBR = f(MC)

4

CBR = 28.93 − 0.220*MC

R² = 0.30

MSE = 12.33

RMSE = 3.51

CBR = f(MDD)

5

CBR = −69.89 + 54.944*MDD

R² = 0.82

MSE = 3.138

RMSE = 1.772

CBR = f(OMC)

6

CBR = 38.676 − 1.386*OMC

R² = 0.9374

MSE = 1.111

RMSE = 1.054

Quadratic Model

y = a₀ + a₁x + a₁x²

CBR = f(PL)

7

CBR = 37.61 − 1.179P.L + 0.02*PL²

R² = 0.11

MSE = 16.69

RMSE = 4.085

CBR = f(LL)

8

CBR = 39.10 − 0.763LL + 0.009*LL²

R² = 0.13

MSE = 16.347

RMSE = 4.043

CBR = f(PI)

9

CBR = 34.97 − 1.096PI + 0.026*PI²

R² = 0.08

MSE = 17.27

RMSE = 4.156

CBR = f(MC)

10

CBR = 36.313 − 0.8023MC + 0.009*MC²

R² = 0.35

MSE = 12.130

RMSE = 3.483

CBR = f(MDD)

11

CBR = −820.11 + 926.06MDD − 252.49*MDD²

R² = 0.897

MSE = 1.929

RMSE = 1.389

CBR = f(OMC)

12

CBR = 30.948 + 0.157OMC − 0.0718*OMC²

R² = 0.96

MSE = 0.74

RMSE = 0.861

Multiple Linear Regression Model

CBR = f(OMC, MDD)

13

CBR = 52.16 − 6.927*MDD − 1.542*OMC

R² = 0.9387

MSE = 1.152

RMSE = 1.073