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Table 2 The Parameter estimates of the tentative models with their AIC and SC

From: Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Model

Variable

Coefficient

Std. Error

t-Statistic

prob

AIC

SC

SARIMA(1,0,1)(0,1,0)12

C

−0.81

0.16

−4.92

< 0.01

  

AR(1)

−0.16

0.22

−0.73

0.47

3.00

3.10

MA(1)

0.51

0.20

2.52

0.01

  

SARIMA(1,0,(2))(0,1,0)12

C

−0.84

0.21

−4.00

< 0.01

  

AR(1)

0.23

0.12

1.93

0.06

3.03

3.12

MA(2)

0.27

0.12

2.23

0.03

  

SARIMA((2),0,1)(0,1,0)12

C

−0.78

0.20

−3.96

< 0.01

  

AR(2)

0.14

0.12

1.21

0.23

2.94

3.04

MA(1)

0.37

0.12

3.15

< 0.01

  

SARIMA((2),0,(2))(0,1,0)12

C

−0.77

0.15

−5.22

< 0.01

  

AR(2)

−0.59

0.10

−6.07

< 0.01

2.87

2.98

MA(2)

0.96

0.02

54.39

< 0.01

  

SARIMA(2,0,(2))(0,1,0)12

C

−0.78

0.16

−4.83

< 0.01

  

AR(1)

0.17

0.10

1.77

0.08

2.87

3.00

AR(2)

−0.61

0.10

−6.26

< 0.01

  

MA(2)

0.97

0.02

46.44

< 0.01

  

SARIMA(2,0,1)(0,1,0)12

C

−0.71

0.22

−3.21

0.00

  

AR(1)

0.64

0.25

2.57

0.01

2.91

3.03

AR(2)

−0.02

0.14

−0.17

0.86

  

MA(1)

−0.31

0.27

−1.15

0.26

  

SARIMA(1,0,2)(0,1,0)12

C

−0.81

0.18

−4.46

< 0.01

  

AR(1)

−0.16

0.29

−0.54

0.59

3.01

3.13

MA(1)

0.50

0.30

1.65

0.10

  

MA(2)

0.18

0.14

1.29

0.20

  

SARIMA(2,0,2)(0,1,0)12

C

−0.72

0.22

−3.31

< 0.01

  

AR(1)

0.65

0.26

2.52

0.01

  

AR(2)

−0.09

0.21

− 0.44

0.66

2.93

3.09

MA(1)

−0.33

0.28

−1.17

0.25

  

MA(2)

0.12

0.23

0.52

0.60