pertemuan 8 (halaman 123-124)



TUGAS
ANALISIS REGRESI Halaman 123-124


Disusun Oleh :
Fentria Angraeni
 (20160302163)






FAKULTAS ILMU KESEHATAN
JURUSAN S1 ILMU GIZI
TAHUN 2017






Latihan Halaman 123
No.1
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

f.        Nilai r2

= 0.050

g.      Model regresi
CHOL = 192.155 + 0.108 TRIG + 0.292 UM






2.LATIHAN Halaman 124
No. 2
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

f.        Nilai r2

= 0.939
g.      Model regresi
BB = -33.412 + 0.210 TB + 1.291 BTL + 0.004

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