TUGAS
PERTEMUAN 14
Halaman
222
ESTIMASI
MODEL 1 : CHOL = 203.123 + 0.127 TRIG
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
1181.676
|
1
|
1181.676
|
1.850
|
.181a
|
Residual
|
27464.768
|
43
|
638.716
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
trigliserida
|
|
|
|
|||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
203.123
|
17.156
|
|
11.840
|
.000
|
trigliserida
|
.127
|
.093
|
.203
|
1.360
|
.181
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
ESTIMASI
MODEL 2 : CHOL = 204.048 + 0.445 UMUR
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
655.625
|
1
|
655.625
|
1.007
|
.321a
|
Residual
|
27990.819
|
43
|
650.949
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
umur
|
|
|
|
|
||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
204.048
|
22.093
|
|
9.236
|
.000
|
Umur
|
.445
|
.444
|
.151
|
1.004
|
.321
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
203.123
|
17.156
|
|
11.840
|
.000
|
trigliserida
|
.127
|
.093
|
.203
|
1.360
|
.181
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
ESTIMASI
MODEL 3 : CHOL = 217.420 + 0.003 UMQS
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
396.227
|
1
|
396.227
|
.603
|
.442a
|
Residual
|
28250.217
|
43
|
656.982
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
umur kuadrat
|
|
|
|
|||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
217.420
|
11.555
|
|
18.816
|
.000
|
umur kuadrat
|
.003
|
.004
|
.118
|
.777
|
.442
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
ESTIMASI
MODEL 4 : CHOL = 192.155 + 0.292 UM + 0.108 TRIG
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
1437.719
|
2
|
718.860
|
1.110
|
.339a
|
Residual
|
27208.725
|
42
|
647.827
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
trigliserida, umur
|
|
|
|
|||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
192.155
|
24.554
|
|
7.826
|
.000
|
Umur
|
.292
|
.464
|
.099
|
.629
|
.533
|
|
trigliserida
|
.108
|
.098
|
.173
|
1.099
|
.278
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
ESTIMASI
MODEL 5 : CHOL = - 25.670 + 9.838 UM - 0.093 UMQS
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
3678.335
|
2
|
1839.167
|
3.094
|
.056a
|
Residual
|
24968.110
|
42
|
594.479
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
umur kuadrat, umur
|
|
|
||||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
T
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
-25.670
|
104.039
|
|
-.247
|
.806
|
umur
|
9.838
|
4.187
|
3.342
|
2.350
|
.024
|
|
umur kuadrat
|
-.093
|
.041
|
-3.207
|
-2.255
|
.029
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
ESTIMASI
MODEL 6 : CHOL = - 21.969 + 9.220 UM + 0.079 TRIG - 0.088
UMQS
ANOVAb
|
||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
4086.344
|
3
|
1362.115
|
2.274
|
.094a
|
Residual
|
24560.100
|
41
|
599.027
|
|
|
|
Total
|
28646.444
|
44
|
|
|
|
|
a. Predictors: (Constant),
umur kuadrat, trigliserida, umur
|
|
|
||||
b. Dependent Variable:
cholesterol
|
|
|
|
Coefficientsa
|
||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
T
|
Sig.
|
||
B
|
Std. Error
|
Beta
|
||||
1
|
(Constant)
|
-21.969
|
104.532
|
|
-.210
|
.835
|
umur
|
9.220
|
4.269
|
3.132
|
2.160
|
.037
|
|
trigliserida
|
.079
|
.095
|
.126
|
.825
|
.414
|
|
umur kuadrat
|
-.088
|
.042
|
-3.035
|
-2.103
|
.042
|
|
a. Dependent Variable:
cholesterol
|
|
|
|
Kita lakukan uji
parsial F seperti berikut (berdasarkan hasil-hasil yang sudah kita lakukan
diatas)
ANOVA Tabel untuk TRIG
dengan CHOL dan UM , UMSQ
Sumber
|
Df
|
SS
|
MS
|
F
|
r2
|
X1
|
1
|
1181.676
|
1181.676
|
1.97266
|
0.143
|
Regresi X2│X1
|
1
|
1.21668
|
1.21668
|
0.00203
|
|
X3│X1, X2
|
1
|
2.84224
|
2.84224
|
0.00474
|
|
Residual
|
41
|
24560.100
|
599.027
|
||
Total
|
44
|
28646.444
|
Nilai F untuk penambahan independent
variabel X3 = 0.00474 < F 4.08 ini berarti hipotesa H0 : β3
= 0 diterima atau gagal ditolak artinya penambahan third order ( X 3)
tidak secara bermakna dapat memprediksi Y.
Kita bersimpulan bahwa :
a.
Penambahan
“ second order” sesuai (fit) dengan
nilai r2 = 0.128
b.
Penambahan
nilai r2 menjadi 0.143
pada “ thind order” hanya sebesar 0.015 adalah kecil
c.
Kurva yang ada cukup
diterangkan dengan “second
order”
TUGAS
HALAMAN 223
Source
|
df
|
SS
|
MS
|
F
|
X
|
1
|
174.473,96
|
174.473,96
|
429,1691
|
Regresi X2│X
|
1
|
10.515,44
|
10515,44
|
25,8658
|
X3│X,X2
|
1
|
415,19
|
415,19
|
1,02128
|
Residual
|
15
|
6098,08
|
406,539
|
|
Total
|
18
|
190.502,93
|
|
|
Model regresi :
Model estimasi 1 : Y = - 122.345 + 6.227
X
Model estimasi 2 : Y = 32.091 – 3.051 X
+ 0.1176 X2
Model estimasi 3 : Y = 114.621 – 10.620
X + 0.3247 X2 + 0.00173 X3
Jawaban :
1.
Nilai
r2 1 :
2.
Nilai
r2 2 :
3.
Nilai
r2 3 :
4. Nilai
F model estimasi 1: 429.19 > F tabel
4.54, maka kesimpulan perubahan penambahan independen variabel X secara
bermakna meningkatkan prediksi Y.
5. Nilai
F model estimasi 2 : 25.87 > F tabel
4.54, maka kesimpulan perubahan penambahan independen variabel X2 secara bermakna meningkatkan prediksi Y.
6. Nilai
F model estimasi 3 : 1.02 > F tabel
4.54, maka kesimpulan perubahan penambahan independen variabel X tidak secara bermakna meningkatkan prediksi
Y.
7. Model
yang terbaik Y = -122.345 + 6.227 X