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Table 3 Summary of Poisson regression models used in this study
From:
Assessing trends and predictors of tuberculosis in Taiwan
Poisson regression model
a
RMSE
Hwalien County
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
mean
,
t
-
3
2.66
(T1)
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
mean
,
t
-
3
+
β
6
a
g
e
t
2.48
(T2)
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
mean
,
t
-
3
+
β
6
a
g
e
t
+
β
7
m
a
l
e
t
+
β
8
f
e
m
a
l
e
t
2.34
(T3)
Taitung County
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
max
,
t
-
2
2.64
(T4)
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
max
,
t
-
2
+
β
6
a
g
e
t
2.63
(T5)
Y
(
t
)
=
exp
β
0
+
β
1
t
+
β
2
t
2
+
β
3
∑
n
=
1
5
sin
(
2
n
π
t
∕
12
)
+
β
4
∑
n
=
1
5
cos
(
2
n
π
t
∕
12
)
+
β
5
T
max
,
t
-
2
+
β
6
a
g
e
t
+
β
7
m
a
l
e
t
+
β
8
f
e
m
a
l
e
t
2.63
(T6)
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