LATIHAN
1. Lakukan
prediksi CHOL dengan variabel independen TRIG dan UM :
a. Hitung
Sum of Square for Regression (X)
b. Hitung
Sum of Square for Residual
c. Hitung
Means Sum of Square for Regression (X)
d. Hitung
Means Sum of Square for Residual
e. Hitung
nilai F
f. Hitung
nilai r2
g. Tulis
Model Regresi
UM
|
CHOL
|
TRIG
|
UM
|
CHOL
|
TRIG
|
UM
|
CHOL
|
TRIG
|
40
|
218
|
194
|
37
|
212
|
140
|
55
|
319
|
191
|
46
|
265
|
188
|
40
|
244
|
132
|
58
|
212
|
216
|
69
|
197
|
134
|
32
|
217
|
140
|
41
|
209
|
154
|
44
|
188
|
155
|
56
|
227
|
279
|
60
|
224
|
198
|
41
|
217
|
191
|
49
|
218
|
101
|
50
|
184
|
129
|
56
|
240
|
207
|
50
|
241
|
213
|
48
|
222
|
115
|
48
|
222
|
155
|
46
|
234
|
168
|
49
|
229
|
148
|
49
|
244
|
235
|
52
|
231
|
242
|
39
|
204
|
164
|
41
|
190
|
167
|
51
|
297
|
142
|
40
|
211
|
104
|
38
|
209
|
186
|
46
|
230
|
240
|
47
|
230
|
218
|
36
|
208
|
179
|
60
|
258
|
173
|
67
|
230
|
239
|
39
|
214
|
129
|
47
|
243
|
175
|
57
|
222
|
183
|
59
|
238
|
220
|
58
|
236
|
199
|
50
|
213
|
190
|
56
|
219
|
155
|
66
|
193
|
201
|
43
|
238
|
259
|
44
|
241
|
201
|
52
|
193
|
193
|
55
|
234
|
156
|
UM = Umur
CHOL = Cholesterol
TRIG = Trigliserida
Jawaban :
Variables Entered/Removed
|
|||
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
Umur, 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
|
.224a
|
.050
|
.005
|
25.452
|
a.
Predictors: (Constant), Umur, Trigliserida
|
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), Umur, Trigliserida
|
||||||
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
|
Trigliserida
|
.108
|
.098
|
.173
|
1.099
|
.278
|
|
Umur
|
.292
|
.464
|
.099
|
.629
|
.533
|
|
a.
Dependent Variable: Cholesterol
|
a.
Hitung
Sum of Square for Regression (X)
SSY
– SSE = 28646.444 – 27208.725 = 1437.719
b.
Hitung
Sum of Square for Residual
c.
Hitung
Means Sum of Square for Regression (X)
SSRegr
/ df = 1437.719 / 2 = 718.860
d.
Hitung
Means Sum of Square for Residual
SSResd
/ df = 27208.725 / 42 = 647.827
e.
Hitung
nilai F
F
= MS – Regr / MS – Resd = 718.860 / 647.827 = 1.110
f.
Hitung
nilai r2 = 0.050
Model Regresi
CHOL
= 192.155+ 0.108 TRIG + 0.292 UM
Perhatikan
nilai t untuk masing-masing parameter dan signifikansinya.
Pada individu yang
berumur 55 tahun dengan TRIG = 156, maka Cholesterol nya diprediksi sebesar :
=
192.155+ (0.108*156) + (0.292*55)
=
192.155+ 16.848 + 16.06
=
225.063 dibulatkan menjadi 225
Pada individu yang
berumur 67 tahun dengan TRIG = 239, maka Cholesterol nya diprediksi sebesar :
=
192.155+ (0.108*239) + (0.292*67)
=
192.155+ 25.812 + 19.564
=
237.531 dibulatkan menjadi 238
2. Lakukan
prediksi Berat Badan (BB) dengan variabel independen Tinggi Badan (TB), Berat
Badan Tanpa Lemak (BTL) dan Asupan Kalori (AK) :
a. Hitung
Sum of Square for Regression (X)
b. Hitung
Sum of Square for Residual
c. Hitung
Means Sum of Square for Regression (X)
d. Hitung
Means Sum of Square for Residual
f. Hitung
nilai r2
g. Tulis
Model Regresi
BB
|
TB
|
BTL
|
AK
|
79.2
|
149
|
54.1
|
2670
|
64.0
|
152
|
44.3
|
820
|
67.0
|
155.7
|
47.8
|
1210
|
78.4
|
159
|
53.9
|
2678
|
66.0
|
163.3
|
47.5
|
1205
|
63.0
|
166
|
43
|
815
|
65.9
|
169
|
47.1
|
1200
|
63.1
|
172
|
44.0
|
1180
|
73.2
|
174.5
|
44.1
|
1850
|
66.5
|
176.1
|
48.3
|
1260
|
61.9
|
176.5
|
43.5
|
1170
|
72.5
|
179
|
43.3
|
1852
|
101.1
|
182
|
66.4
|
1790
|
66.2
|
170.4
|
47.5
|
1250
|
99.9
|
184.9
|
66
|
1889
|
63.0
|
169
|
44
|
915
|
BB = Berat Badan
TB = Tinggi Badan
BTL = Berat Tanpa Lemak
AK = Asupan Kalori
Jawaban :
Variables Entered/Removed
|
|||
Model
|
Variables
Entered
|
Variables
Removed
|
Method
|
1
|
Asupan
Kalori, Tinggi Badan, Berat Tanpa Lemaka
|
.
|
Enter
|
a. All requested
variables entered.
b. Dependent
Variable: Berat Badan
|
Model Summary
|
||||
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
1
|
.969a
|
.939
|
.923
|
3.4224
|
a. Predictors:
(Constant), Asupan Kalori, Tinggi Badan, Berat Tanpa Lemak
|
ANOVAb
|
||||||
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
1
|
Regression
|
2148.400
|
3
|
716.133
|
61.141
|
.000a
|
Residual
|
140.554
|
12
|
11.713
|
|
|
|
Total
|
2288.954
|
15
|
|
|
|
|
a.
Predictors: (Constant), Asupan Kalori, Tinggi Badan, Berat Tanpa Lemak
|
||||||
b. Dependent
Variable: Berat Badan
|
Coefficientsa
|
||||||
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
B
|
Std.
Error
|
Beta
|
||||
1
|
(Constant)
|
-33.412
|
14.489
|
|
-2.306
|
.040
|
Tinggi
Badan
|
.210
|
.090
|
.180
|
2.339
|
.037
|
|
Berat
Tanpa Lemak
|
1.291
|
.150
|
.785
|
8.631
|
.000
|
|
Asupan
Kalori
|
.004
|
.002
|
.209
|
2.375
|
.035
|
|
a. Dependent
Variable: Berat Badan
|
a.
Hitung
Sum of Square for Regression (X)
SSY
– SSE = 2288.954 – 140.554
= 2148.400
b.
Hitung
Sum of Square for Residual
c.
Hitung
Means Sum of Square for Regression (X)
SSRegr
/ df = 2148.400/ 3 = 716.133
d.
Hitung
Means Sum of Square for Residual
SSResd
/ df = 140.554/ 12 = 11.713
e.
Hitung
nilai F
F
= MS – Regr / MS – Resd = 716.133/ 11.713
= 61.140
f.
Hitung
nilai r2 = 0.939
Model Regresi
BB
= -33.412 + 0.210 TB + 1.291 BTL + 0.004 AK
Perhatikan
nilai t untuk masing-masing parameter dan signifikansinya.
Pada individu
memiliki Tinggi Badan = 184.9 dengan Berat Badan Tanpa Lemak = 66 dan Asupan
Kalori = 1889, maka Berat badan nya diprediksi sebesar :
=
-33.412 + (0.210*184.9) + (1.291*66) + (0.004*1889)
=
-33.412 + 38.829 + 85.206 + 7.556
=
98.179 dibulatkan menjadi 98
Pada individu
memiliki Tinggi Badan = 152 dengan Berat Badan Tanpa Lemak = 44.3 dan Asupan
Kalori = 820, maka Berat badan nya diprediksi sebesar :
=
-33.412 + (0.210*152) + (1.291*44.3) + (0.004*820)
=
-33.412 + 31.92 + 57.1913 + 3.28
=
58.9793 dibulatkan menjadi 59
LATIHAN
1. Lakukan
prediksi CHOL dengan variabel independen TRIG dan UM :
a. Hitung
Sum of Square for Regression (X)
b. Hitung
Sum of Square for Residual
c. Hitung
Means Sum of Square for Regression (X)
d. Hitung
Means Sum of Square for Residual
e. Hitung
nilai F
f. Hitung
nilai r2
g. Tulis
Model Regresi
UM
|
CHOL
|
TRIG
|
UM
|
CHOL
|
TRIG
|
UM
|
CHOL
|
TRIG
|
40
|
218
|
194
|
37
|
212
|
140
|
55
|
319
|
191
|
46
|
265
|
188
|
40
|
244
|
132
|
58
|
212
|
216
|
69
|
197
|
134
|
32
|
217
|
140
|
41
|
209
|
154
|
44
|
188
|
155
|
56
|
227
|
279
|
60
|
224
|
198
|
41
|
217
|
191
|
49
|
218
|
101
|
50
|
184
|
129
|
56
|
240
|
207
|
50
|
241
|
213
|
48
|
222
|
115
|
48
|
222
|
155
|
46
|
234
|
168
|
49
|
229
|
148
|
49
|
244
|
235
|
52
|
231
|
242
|
39
|
204
|
164
|
41
|
190
|
167
|
51
|
297
|
142
|
40
|
211
|
104
|
38
|
209
|
186
|
46
|
230
|
240
|
47
|
230
|
218
|
36
|
208
|
179
|
60
|
258
|
173
|
67
|
230
|
239
|
39
|
214
|
129
|
47
|
243
|
175
|
57
|
222
|
183
|
59
|
238
|
220
|
58
|
236
|
199
|
50
|
213
|
190
|
56
|
219
|
155
|
66
|
193
|
201
|
43
|
238
|
259
|
44
|
241
|
201
|
52
|
193
|
193
|
55
|
234
|
156
|
UM = Umur
CHOL = Cholesterol
TRIG = Trigliserida
Jawaban :
Variables Entered/Removed
|
|||
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
Umur, 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
|
.224a
|
.050
|
.005
|
25.452
|
a.
Predictors: (Constant), Umur, Trigliserida
|
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), Umur, Trigliserida
|
||||||
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
|
Trigliserida
|
.108
|
.098
|
.173
|
1.099
|
.278
|
|
Umur
|
.292
|
.464
|
.099
|
.629
|
.533
|
|
a.
Dependent Variable: Cholesterol
|
a.
Hitung
Sum of Square for Regression (X)
SSY
– SSE = 28646.444 – 27208.725 = 1437.719
b.
Hitung
Sum of Square for Residual
c.
Hitung
Means Sum of Square for Regression (X)
SSRegr
/ df = 1437.719 / 2 = 718.860
d.
Hitung
Means Sum of Square for Residual
SSResd
/ df = 27208.725 / 42 = 647.827
e.
Hitung
nilai F
F
= MS – Regr / MS – Resd = 718.860 / 647.827 = 1.110
f.
Hitung
nilai r2 = 0.050
Model Regresi
CHOL
= 192.155+ 0.108 TRIG + 0.292 UM
Perhatikan
nilai t untuk masing-masing parameter dan signifikansinya.
Pada individu yang
berumur 55 tahun dengan TRIG = 156, maka Cholesterol nya diprediksi sebesar :
=
192.155+ (0.108*156) + (0.292*55)
=
192.155+ 16.848 + 16.06
=
225.063 dibulatkan menjadi 225
Pada individu yang
berumur 67 tahun dengan TRIG = 239, maka Cholesterol nya diprediksi sebesar :
=
192.155+ (0.108*239) + (0.292*67)
=
192.155+ 25.812 + 19.564
=
237.531 dibulatkan menjadi 238
2. Lakukan
prediksi Berat Badan (BB) dengan variabel independen Tinggi Badan (TB), Berat
Badan Tanpa Lemak (BTL) dan Asupan Kalori (AK) :
a. Hitung
Sum of Square for Regression (X)
b. Hitung
Sum of Square for Residual
c. Hitung
Means Sum of Square for Regression (X)
d. Hitung
Means Sum of Square for Residual
f. Hitung
nilai r2
g. Tulis
Model Regresi
BB
|
TB
|
BTL
|
AK
|
79.2
|
149
|
54.1
|
2670
|
64.0
|
152
|
44.3
|
820
|
67.0
|
155.7
|
47.8
|
1210
|
78.4
|
159
|
53.9
|
2678
|
66.0
|
163.3
|
47.5
|
1205
|
63.0
|
166
|
43
|
815
|
65.9
|
169
|
47.1
|
1200
|
63.1
|
172
|
44.0
|
1180
|
73.2
|
174.5
|
44.1
|
1850
|
66.5
|
176.1
|
48.3
|
1260
|
61.9
|
176.5
|
43.5
|
1170
|
72.5
|
179
|
43.3
|
1852
|
101.1
|
182
|
66.4
|
1790
|
66.2
|
170.4
|
47.5
|
1250
|
99.9
|
184.9
|
66
|
1889
|
63.0
|
169
|
44
|
915
|
BB = Berat Badan
TB = Tinggi Badan
BTL = Berat Tanpa Lemak
AK = Asupan Kalori
Jawaban :
Variables Entered/Removed
|
|||
Model
|
Variables
Entered
|
Variables
Removed
|
Method
|
1
|
Asupan
Kalori, Tinggi Badan, Berat Tanpa Lemaka
|
.
|
Enter
|
a. All requested
variables entered.
b. Dependent
Variable: Berat Badan
|
Model Summary
|
||||
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
1
|
.969a
|
.939
|
.923
|
3.4224
|
a. Predictors:
(Constant), Asupan Kalori, Tinggi Badan, Berat Tanpa Lemak
|
ANOVAb
|
||||||
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
1
|
Regression
|
2148.400
|
3
|
716.133
|
61.141
|
.000a
|
Residual
|
140.554
|
12
|
11.713
|
|
|
|
Total
|
2288.954
|
15
|
|
|
|
|
a.
Predictors: (Constant), Asupan Kalori, Tinggi Badan, Berat Tanpa Lemak
|
||||||
b. Dependent
Variable: Berat Badan
|
Coefficientsa
|
||||||
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
B
|
Std.
Error
|
Beta
|
||||
1
|
(Constant)
|
-33.412
|
14.489
|
|
-2.306
|
.040
|
Tinggi
Badan
|
.210
|
.090
|
.180
|
2.339
|
.037
|
|
Berat
Tanpa Lemak
|
1.291
|
.150
|
.785
|
8.631
|
.000
|
|
Asupan
Kalori
|
.004
|
.002
|
.209
|
2.375
|
.035
|
|
a. Dependent
Variable: Berat Badan
|
a.
Hitung
Sum of Square for Regression (X)
SSY
– SSE = 2288.954 – 140.554
= 2148.400
b.
Hitung
Sum of Square for Residual
c.
Hitung
Means Sum of Square for Regression (X)
SSRegr
/ df = 2148.400/ 3 = 716.133
d.
Hitung
Means Sum of Square for Residual
SSResd
/ df = 140.554/ 12 = 11.713
e.
Hitung
nilai F
F
= MS – Regr / MS – Resd = 716.133/ 11.713
= 61.140
f.
Hitung
nilai r2 = 0.939
Model Regresi
BB
= -33.412 + 0.210 TB + 1.291 BTL + 0.004 AK
Perhatikan
nilai t untuk masing-masing parameter dan signifikansinya.
Pada individu
memiliki Tinggi Badan = 184.9 dengan Berat Badan Tanpa Lemak = 66 dan Asupan
Kalori = 1889, maka Berat badan nya diprediksi sebesar :
=
-33.412 + (0.210*184.9) + (1.291*66) + (0.004*1889)
=
-33.412 + 38.829 + 85.206 + 7.556
=
98.179 dibulatkan menjadi 98
Pada individu
memiliki Tinggi Badan = 152 dengan Berat Badan Tanpa Lemak = 44.3 dan Asupan
Kalori = 820, maka Berat badan nya diprediksi sebesar :
=
-33.412 + (0.210*152) + (1.291*44.3) + (0.004*820)
=
-33.412 + 31.92 + 57.1913 + 3.28
=
58.9793 dibulatkan menjadi 59
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