1. 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
Variables Entered/Removedb
|
|||
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
|
Koefisien regresi = r2
= 0.050
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
|
|
|
|
Prediksi CHOL dengan variable independen TRIG dan UM :
a.
Sum of
Square for Regression (X)
= Sum of
Square Total - Sum of Square Residual
= 28646.444 - 27208.725
= 1437.719
b.
Sum of
Square for Residual
= Sum of
Square Total - Sum of Square of Regression
= 28646.444 - 1437.719
= 27208.725
c.
Means Sum
of Square for Regression (X)
= Sum of
Square for Regression / df Regresi
= 1437.719 / 2
= 718.860
d.
Means Sum
of Square for Residual
= Sum of
Square for Residual / df Residual
= 27208.725 / 42
= 647.827
e.
Nilai F
= Means Sum of
Square for Regression / Means Sum of Square for Residual
= 718.860 / 647.827
=
1.110
g.
Model
regresi
CHOL = 192.155 + 0.108 TRIG + 0.292 UM
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
e.
Hitung
nilai F
f.
Hitung
nilai r2
g.
Tulis model
regresi
BB
|
TB
|
BTL
|
AK
|
BB
|
TB
|
BTL
|
AK
|
79.2
|
149.0
|
54.1
|
2670.0
|
73.2
|
174.5
|
44.1
|
1850.0
|
64.0
|
152.0
|
44.3
|
820.0
|
66.5
|
176.1
|
48.3
|
1260.0
|
67.0
|
155.7
|
47.8
|
1210.0
|
61.9
|
176.5
|
43.5
|
1170.0
|
78.4
|
159.0
|
53.9
|
2678.0
|
72.5
|
179.0
|
43.3
|
1852.0
|
66.0
|
163.3
|
47.5
|
1205.0
|
101.1
|
182.0
|
66.4
|
1790.0
|
63.0
|
166.0
|
43.0
|
815.0
|
66.2
|
170.4
|
47.5
|
1250.0
|
65.9
|
169.0
|
47.1
|
1200.0
|
99.9
|
184.9
|
66.0
|
1889.0
|
63.1
|
172.0
|
44.0
|
1180.0
|
63.0
|
169.0
|
44.0
|
915.0
|
BB =
Berat Badan
TB =
Tinggi Badan
BTL = Berat
Tanpa Lemak
AK =
Asupan Kalori
Variables Entered/Removedb
|
|||
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
|
Koefisien regresi = r2 = 0.939
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
|
|
|
|
|
Prediksi Berat Badan (BB) dengan variable independen Tinggi Badan
(TB), Berat Badan tanpa Lemak (BTL) dan Asupan Kalori (AK)
a.
Sum of
Square for Regression (X)
= Sum of
Square Total - Sum of Square Residual
= 2288.954 - 140.554
= 2148.400
b.
Sum of
Square for Residual
= Sum of
Square Total - Sum of Square of Regression
= 2288.954 - 2148.400
= 140.554
c.
Means Sum
of Square for Regression (X)
= Sum of
Square for Regression / df Regresi
= 2148.400 / 3
= 716.133
d.
Means Sum
of Square for Residual
= Sum of
Square for Residual / df Residual
= 140.554 / 12
= 11.713
e.
Nilai F
= Means Sum of
Square for Regression / Means Sum of Square for Residual
= 7716.133 / 11.713
=
61.141
g. Model
regresi
BB
= -33.412 + 0.210
TB + 1.291 BTL + 0.004 AK.
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