Analisis Regresi Ke-3
MATA KULIAH ANALISIS REGRESI
TUGAS PERTEMUAN 1
PRODI ILMU GIZI FAKULTAS
ILMU-ILMU KESEHATAN
Oleh : Kurnia
sari
(20170302118)
Dosen mata kuliah
: Idrus
Jus’at, Ph.D
Latihan 1.
Kasus
|
IMT
|
GPP
|
Kasus
|
IMT
|
GPP
|
Kasus
|
IMT
|
GPP
|
1
|
18.6
|
150
|
10
|
18.2
|
120
|
19
|
27
|
140
|
2
|
28.1
|
150
|
11
|
17.9
|
130
|
20
|
18.9
|
100
|
3
|
25.1
|
120
|
12
|
21.8
|
140
|
21
|
16.7
|
100
|
4
|
21.6
|
150
|
13
|
16.1
|
100
|
22
|
18.5
|
170
|
5
|
28.4
|
190
|
14
|
21.5
|
150
|
23
|
19.4
|
150
|
6
|
20.8
|
110
|
15
|
24.5
|
130
|
24
|
24.0
|
160
|
7
|
23.2
|
150
|
16
|
23.7
|
180
|
25
|
26.8
|
200
|
8
|
15.9
|
130
|
17
|
21.9
|
140
|
26
|
28.7
|
190
|
9
|
16.4
|
130
|
18
|
18.6
|
135
|
27
|
21.0
|
120
|
Regression
Variables Entered/Removed
|
|||
Model
|
Variables
Entered
|
Variables
Removed
|
Method
|
1
|
IMTb
|
.
|
Enter
|
a. Dependent Variable:
GPP
|
|||
b. All requested
variables entered.
|
Model Summary
|
||||
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
1
|
.628a
|
.394
|
.370
|
21.629
|
a. Predictors:
(Constant), IMT
|
ANOVA
|
||||||
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
1
|
Regression
|
7617.297
|
1
|
7617.297
|
16.282
|
.000b
|
Residual
|
11695.666
|
25
|
467.827
|
|||
Total
|
19312.963
|
26
|
||||
a. Dependent Variable:
GPP
|
||||||
b. Predictors:
(Constant), IMT
|
Coefficients
|
||||||
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
B
|
Std.
Error
|
Beta
|
||||
1
|
(Constant)
|
48.737
|
23.494
|
2.074
|
.048
|
|
IMT
|
4.319
|
1.070
|
.628
|
4.035
|
.000
|
|
a.
Dependent Variable: GPP
Persamaan Garis:
|
Latihan
2.
Subjek
|
Berat Badan (kg)
|
Glukosa mg/100ml
|
Subjek
|
Berat Badan (kg)
|
Glukosa mg/100ml
|
1
|
64.0
|
108
|
9
|
82.1
|
101
|
2
|
75.3
|
109
|
10
|
78.9
|
85
|
3
|
73.0
|
104
|
11
|
76.7
|
99
|
4
|
82.1
|
102
|
12
|
82.1
|
100
|
5
|
76.2
|
105
|
13
|
83.9
|
108
|
6
|
95.7
|
121
|
14
|
73.0
|
104
|
7
|
59.4
|
79
|
15
|
64.4
|
102
|
8
|
93.4
|
107
|
16
|
77.6
|
87
|
Regression
Variables Entered/Removed
|
|||
Model
|
Variables
Entered
|
Variables
Removed
|
Method
|
1
|
berat badanb
|
.
|
Enter
|
a. Dependent Variable:
glukosa
|
|||
b. All requested
variables entered.
|
Model Summary
|
||||
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
1
|
.484a
|
.234
|
.180
|
9.276
|
a. Predictors:
(Constant), berat badan
|
ANOVA
|
||||||
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
1
|
Regression
|
368.798
|
1
|
368.798
|
4.286
|
.057b
|
Residual
|
1204.639
|
14
|
86.046
|
|||
Total
|
1573.438
|
15
|
||||
a. Dependent Variable:
glukosa
|
||||||
b. Predictors:
(Constant), berat badan
|
Coefficients
|
||||||
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
B
|
Std.
Error
|
Beta
|
||||
1
|
(Constant)
|
61.877
|
19.189
|
3.225
|
.006
|
|
berat badan
|
.510
|
.246
|
.484
|
2.070
|
.057
|
|
a. Dependent Variable:
glukosa
|
Persamaan garis:
Glukosa
= 61.877 + 0.510 Berat Badan
Latihan
3.
1. Jelaskan
asumsi-asumsi tentang analisa regresi sederhana bila kita ingin membuat
inferensi tentang populasi dari data yang kita punyai.
2. Mengapa
persamaan regresi disebut “the least square equation”?
3. Jelaskan
tentang pada persamaan regresi.
4. Jelaskan
tentang pada persamaan regresi.
2. menentukan garis lurus yang terbaik. Teknik ini
menggunakan “penentuan garis dengan error yang minimal” berdasarkan titik
observasi dalam diagram sebar. Karena semakin kecil penyimpangan astu observasi
terhadap garis lurus (atau semakin kecil kuadrat simpangan) semakin dekat garis
lurus yang terbaik yang diperoleh dari data yang dimiliki.
2.
Intersep () adalah
nilai Y bila nilai X = 0
3.
Slop () berarti
setiap kenaikan 1 unit nilai X maka nilai Y akan bertambah (meningkat) sebesar . Sebaliknya negatif (- ) maka
kenaikan 1 unit nilai X maka nilai Y akan menurun sebesar .
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