Jumat, 30 Desember 2016

TUGAS ANALISIS REGRESI PERTEMUAN 13

RONA CHELSEA NIM 201532287
ANALISIS REGRESI TUGAS 13     
Latihan 2 hal.222 ,223 dan BAB 9.
 
 
Lakukan prediksi CHOL dengan variabel Independet TRIGLI, UM dan UM Kuadrat 
a.     Lakukan analisis Regresi masing-masing Independent variabel
b.     Hitung SS Regression ( X3,X1, X2)
c.      Hitung SS for Residual
d.     Hitung Mean SS for Regression ( X3,X1, X2)
e.      Hitung Mean SS for Residual
f.       Hitung nilai F parsial
g.     Hitung nilai r2
h.     Buktikan penambahan X3 berperan dalam  memprediksi Y
UM
CHOL
TRIG
40
218
194
46
265
188
69
197
134
44
188
155
41
217
191
56
240
207
48
222
155
49
244
235
41
190
167
38
209
186
36
208
179
39
214
129
59
238
220
56
219
155
44
241
201
37
212
140
40
244
132
32
217
140
56
227
279
49
218
101
50
241
213
46
234
168
52
231
242
51
297
142
46
230
240
60
258
173
47
243
175
58
236
199
66
193
201
52
193
193
55
319
191
58
212
216
41
209
154
60
224
198
50
184
129
48
222
115
49
229
148
39
204
164
40
211
104
47
230
218
67
230
239
57
222
183
50
213
190
43
238
259
55
234
156
MODEL 1 : CHOL = β0 + β1 TRIG
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Trigliseridaa
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.203a
.041
.019
25.273
a. Predictors: (Constant), Trigliserida
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
MODEL REGRESI : CHOL = 203.123 + 0.127 TRIG
MODEL 2 : CHOL = β0 + β1 UM
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Umura
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.151a
.023
.000
25.514
a. Predictors: (Constant), 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
MODEL REGRESI : CHOL = 204.048  + 0.445 UM
           

MODEL 3 : CHOL = β0 + β1 UMKWT
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Umur Kuadrata
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.118a
.014
-.009
25.632
a. Predictors: (Constant), Umur Kuadrat

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
MODEL REGRESI : CHOL = 217.420  + 0.003 UMKWT
           

MODEL 4 : CHOL = β0 + β1 TRIG + β2 UM
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Trigliserida, Umura
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.224a
.050
.005
25.452
a. Predictors: (Constant), Trigliserida, Umur
ANOVAb
rModel
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
MODEL REGRESI : CHOL = 192.155  + 0.108 TRI G + 0.292 UM
MODEL 5 : CHOL = β0 + β1 TRIG + β2 UMKWT
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Trigliserida, Umur Kuadrata
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.212a
.045
.000
25.520
a. Predictors: (Constant), Trigliserida, Umur Kuadrat
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1292.618
2
646.309
.992
.379a
Residual
27353.826
42
651.282
Total
28646.444
44
a. Predictors: (Constant), Trigliserida, Umur Kuadrat
b. Dependent Variable: Cholesterol
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
200.525
18.433
10.879
.000
Umur Kuadrat
.002
.005
.065
.413
.682
Trigliserida
.115
.098
.185
1.173
.247
a. Dependent Variable: Cholesterol
ESTIMASI MODEL REGRESI : CHOL = 200.525  + 0.115 TRIG  + 0.002 UMKWT


MODEL 6 : CHOL = β0 + β1 TRIG + β2 UM + β3 UMKWT
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
Method
1
Umur, Trigliserida, Umur Kuadrata
.
Enter
a. All requested variables entered.
b. Dependent Variable: Cholesterol
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.378a
.143
.080
24.475
a. Predictors: (Constant), Umur, Trigliserida, Umur Kuadrat
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, Trigliserida, Umur Kuadrat
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 Kuadrat
-.088
.042
-3.035
-2.103
.042
Trigliserida
.079
.095
.126
.825
.414
Umur
9.220
4.269
3.132
2.160
.037
a. Dependent Variable: Cholesterol
MODEL REGRESI : CHOL = - 21.969  + 0.079 TRI + 9.220 UM – 0.088 UMKWT
Kita lakukan  uji parsial F seperti berikut (berdasarkan  hasil-hasil yang sudah kita lakukan diatas)
ANOVA Tabel untuk TRIG dengan CHOL dan UM , UMKWT
Sumber
Df
SS
MS
F
r2
X1
1
1181.676
1181.676
1.973
0.143
Regresi X2│X1
1
256.043
256.043
0.427
X3│X1, X2
1
2.648,625
2.648,625
4.422
Residual
41
24560.100
599.027

Total
44
28646.444


No
Model Estimasi
F
r 2
1
Y= 203.123 + 0.127 TRIG
                      (0.093)
1.850
0.041
2
Y= 204.048 + 0.445 UM
                      (0.444)
1.007
0.023
3
Y= 217.420 + 0.003 UMKWT
                      (0.004)
0.603
0.014
4
Y= 192.155 + 0.108 TRI + 0.292 UM
                      (0.098)         (0.464)
1.110
0.050
5
Y= 200.525 + 0.115 TRIG + 0.002 UMKWT
                      (0.098)           (0.005)
0.992
0.045
6
Y= 21.969 + 0.079 TRI + 9.220 UM – 0.088 UMKWT
                    (0.095)         (4.269)*         (0.042)*
2.274
0.143
Angka didalam  tanda kurung adalah standar eror
*bermakna ( p<0.05)
Buktikan Penambahan  X3 berperan Dalam memprediksi Y
            Nilai F untuk penambahan independent variabel X3 = 4.222 >  F tabel 4.08 ini berarti hipotesa H0 ditolak artinya penambahan Umur Kuadrat ( X 3) secara bermakna dapat memprediksi Y.
Model Regresi : CHOL = - 21.969 + 0.079 TRIG  + 9.220 UM – 0.088 UMKWT 
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”
HALAMAN 223
LATIHAN 3
Data Informasi sebagai berikut :
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
1.    Tabel Anova
Source
df
SS
MS
F
X
1
174.473,96
174.473,96
429,1691
Regresi X2│X1
1
10.525,44
10.515,44
25,8658
X3│X1, X2
1
415.19
415.19
1,02128
Residual
15
6098.08
406.538
Total
18
190.502,93
2.    Tentukan Besaran r 2 Untuk ketiga model estimasi :
3.Nilai F untuk ketiga Model estimasi dan buat kesimpulan :
·    Nilai F model estimasi 1:  429.19 > F tabel 4.54, maka kesimpulan perubahan penambahan independen variabel X secara bermakna meningkatkan prediksi Y.
·     Nilai F model estimasi 2 : 25.87  > F tabel 4.54, maka kesimpulan perubahan penambahan independen variabel X2  secara bermakna meningkatkan prediksi Y.
·         Nilai F model estimasi 3 : 1.02  > F tabel 4.54, maka kesimpulan perubahan penambahan independen variabel X  tidak secara bermakna meningkatkan prediksi Y.
4.    Model yang terbaik Dari ketiganya : Y = -122.345 + 6.227 X

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