Halaman 222
ESTIMASI MODEL 1 : CHOL = 203.123 + 0.127 TRIG
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 1181.676 1 1181.676 1.850 .181 a
Residual 27464.768 43 638.716
Total 28646.444 44
a. Predictors: (Constant), trigliserida
b. Dependent Variable: cholesterol
Coefficients a
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
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 655.625 1 655.625 1.007 .321 a
Residual 27990.819 43 650.949
Total 28646.444 44
a. Predictors: (Constant), umur
b. Dependent Variable: cholesterol
Coefficients a
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
Coefficients a
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
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 396.227 1 396.227 .603 .442 a
Residual 28250.217 43 656.982
Total 28646.444 44
a. Predictors: (Constant), umur kuadrat
b. Dependent Variable: cholesterol
Coefficients a
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
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 1437.719 2 718.860 1.110 .339 a
Residual 27208.725 42 647.827
Total 28646.444 44
a. Predictors: (Constant), trigliserida, umur
b. Dependent Variable: cholesterol
Coefficients a
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
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 3678.335 2 1839.167 3.094 .056 a
Residual 24968.110 42 594.479
Total 28646.444 44
a. Predictors: (Constant), umur kuadrat, umur
b. Dependent Variable: cholesterol
Coefficients a
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
ANOVA b
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 4086.344 3 1362.115 2.274 .094 a
Residual 24560.100 41 599.027
Total 28646.444 44
a. Predictors: (Constant), umur kuadrat, trigliserida, umur
b. Dependent Variable: cholesterol
Coefficients a
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 r 2
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 X 3 = 0.00474 < F 4.08 ini berarti hipotesa H 0 :
β 3 = 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 r 2 = 0.128
b. Penambahan nilai r 2 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 X 2 │X 1 10.515,44 10515,44 25,8658
X 3 │X,X 2 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 X 2
Model estimasi 3 : Y = 114.621 – 10.620 X + 0.3247 X 2 + 0.00173 X 3
Jawaban :
1. Nilai r 2 1 :
2. Nilai r 2 2 :
3. Nilai r 2 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 X 2 secara bermakna meningkatkan prediksi Y.
oktrima kurnianto
Minggu, 09 Juli 2017
Sabtu, 08 Juli 2017
ANALISIS REGRESI HALAMAN 221
Analisis Regresi pertemuan 13
TUGAS ANALISIS REGRESI HALAMAN 221
Lakukan prediksi TRI dengan variabel independen IMT, Umur dan Umur kuadrat
Bekerja bersama di Laboratorium
- Lakukan analisa regresi masing-masing independen variabel
- Hitung SS for Regression (X3|X1,X2)
- Hitung SS for Residual
- Hitung mean SS for Regression (X3|X1,X2)
- Hitung mean SS for Residual
- Hitung nilai F parsial
- Hitung nilai r2
ANOVAb
| ||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
601.667
|
1
|
601.667
|
.371
|
.547a
|
Residual
|
48697.302
|
30
|
1623.243
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), indeks massa tubuh
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
167.677
|
46.066
|
3.640
|
.001
| |
indeks massa tubuh
|
-.792
|
1.300
|
-.110
|
-.609
|
.547
| |
a. Dependent Variable: trigliserida
|
ESTIMASI MODEL 2 : TRIG = 149.943 - 0.177 UMUR
ANOVAb
| ||||||
Model
|
Sum of Squares
|
Df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
212.189
|
1
|
212.189
|
.130
|
.721a
|
Residual
|
49086.780
|
30
|
1636.226
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), umur
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
149.943
|
28.605
|
5.242
|
.000
| |
umur
|
-.177
|
.492
|
-.066
|
-.360
|
.721
| |
a. Dependent Variable: trigliserida
|
ESTIMASI MODEL 3 : TRIG = 142.230 + 0.000 UMUR KUADRAT
ANOVAb
| ||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
85.385
|
1
|
85.385
|
.052
|
.821a
|
Residual
|
49213.584
|
30
|
1640.453
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), umur kuadrat
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
142.230
|
12.226
|
11.634
|
.000
| |
umur kuadrat
|
.000
|
.003
|
-.042
|
-.228
|
.821
| |
a. Dependent Variable: trigliserida
|
ESTIMASI MODEL 4 : 167.688 - 0.784 IMT - 0.005 UMUR
ANOVAb
| ||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
601.777
|
2
|
300.889
|
.179
|
.837a
|
Residual
|
48697.191
|
29
|
1679.213
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), umur, indeks massa tubuh
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
167.688
|
46.872
|
3.578
|
.001
| |
indeks massa tubuh
|
-.784
|
1.628
|
-.109
|
-.482
|
.634
| |
Umur
|
-.005
|
.613
|
-.002
|
-.008
|
.994
| |
a. Dependent Variable: trigliserida
|
ESTIMASI MODEL 5 : 168.623 - 0.841 IMT + 0.000 UMUR KUADRAT
ANOVAb
| ||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
609.613
|
2
|
304.806
|
.182
|
.835a
|
Residual
|
48689.356
|
29
|
1678.943
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), umur kuadrat, indeks massa tubuh
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
168.623
|
48.827
|
3.453
|
.002
| |
indeks massa tubuh
|
-.841
|
1.505
|
-.117
|
-.559
|
.581
| |
umur kuadrat
|
.000
|
.003
|
.014
|
.069
|
.946
| |
a. Dependent Variable: trigliserida
|
ESTIMASI MODEL 6 : 214.510 - 0.107 IMT - 1.886 UMUR + 0.010 UMUR KUADRAT
ANOVAb
| ||||||
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
| |
1
|
Regression
|
1002.559
|
3
|
334.186
|
.194
|
.900a
|
Residual
|
48296.409
|
28
|
1724.872
| |||
Total
|
49298.969
|
31
| ||||
a. Predictors: (Constant), umur kuadrat, indeks massa tubuh, umur
| ||||||
b. Dependent Variable: trigliserida
|
Coefficientsa
| ||||||
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
| ||
B
|
Std. Error
|
Beta
| ||||
1
|
(Constant)
|
214.510
|
108.129
|
1.984
|
.057
| |
indeks massa tubuh
|
-.107
|
2.166
|
-.015
|
-.050
|
.961
| |
Umur
|
-1.886
|
3.951
|
-.699
|
-.477
|
.637
| |
umur kuadrat
|
.010
|
.022
|
.653
|
.482
|
.634
| |
a. Dependent Variable: trigliserida
|
Kita lakukan uji parsial F seperti berikut (berdasarkan hasil-hasil yang sudah kita lakukan diatas)
ANOVA Tabel untuk TRIG dengan IMT dan UM , UMSQ
Sumber
|
Df
|
SS
|
MS
|
F
|
r2
|
X1
|
1
|
601.667
|
601.667
|
0.34881
|
0.900
|
Regresi X2│X1
|
1
|
1.00018
|
1.00018
|
0.00058
| |
X3│X1, X2
|
1
|
1.66600
|
1.66600
|
0.00966
| |
Residual
|
28
|
48296.409
|
1724.872
| ||
Total
|
31
|
49298.969
|
Nilai F untuk penambahan independent variabel X3 = 0.00966 < F 4.02 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 = 0.021
b. Penambahan nilai r2 menjadi 0.900 pada “ thind order” hanya sebesar 0879 adalah kecil
c. Kurva yang ada cukup diterangkan dengan “second order”
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