Minggu, 16 Juli 2017

Analisis Regresi (Pertemuan 13)

TUGAS HALAMAN  221
ESTIMASI MODEL 1 : TRIG =167.677 - 0.792 IMT
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
601.667
1
601.667
.371
.547a
Residual
48697.302
30
1623.243
Total
49298.969
31
a. Predictors: (Constant), indeksmassatubuh
b. Dependent Variable: trigliserida
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
167.677
46.066
3.640
.001
indeksmassatubuh
-.792
1.300
-.110
-.609
.547
a. Dependent Variable: trigliserida
ESTIMASI MODEL 2 : TRIG = 149.943 - 0.177 UMUR
ANOVAb
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
212.189
1
212.189
.130
.721a
Residual
49086.780
30
1636.226
Total
49298.969
31
a. Predictors: (Constant), umur
b. Dependent Variable: trigliserida
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
149.943
28.605
5.242
.000
umur
-.177
.492
-.066
-.360
.721
a. Dependent Variable: trigliserida
ESTIMASI MODEL 3 : TRIG = 142.230 + 0.000 UMUR KUADRAT
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
85.385
1
85.385
.052
.821a
Residual
49213.584
30
1640.453
Total
49298.969
31
a. Predictors: (Constant), umurkuadrat
b. Dependent Variable: trigliserida
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
142.230
12.226
11.634
.000
umurkuadrat
.000
.003
-.042
-.228
.821
a. Dependent Variable: trigliserida
ESTIMASI MODEL 4 :167.688 - 0.784 IMT - 0.005 UMUR
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
601.777
2
300.889
.179
.837a
Residual
48697.191
29
1679.213
Total
49298.969
31
a. Predictors: (Constant), umur, indeksmassatubuh
b. Dependent Variable: trigliserida
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
167.688
46.872
3.578
.001
indeksmassatubuh
-.784
1.628
-.109
-.482
.634
Umur
-.005
.613
-.002
-.008
.994
a. Dependent Variable: trigliserida
ESTIMASI MODEL 5 :168.623 - 0.841 IMT + 0.000 UMUR KUADRAT
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
609.613
2
304.806
.182
.835a
Residual
48689.356
29
1678.943
Total
49298.969
31
a. Predictors: (Constant), umurkuadrat, indeksmassatubuh
b. Dependent Variable: trigliserida
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
168.623
48.827
3.453
.002
indeksmassatubuh
-.841
1.505
-.117
-.559
.581
umurkuadrat
.000
.003
.014
.069
.946
a. Dependent Variable: trigliserida
ESTIMASI MODEL 6 :214.510 - 0.107 IMT - 1.886 UMUR + 0.010 UMUR KUADRAT
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1002.559
3
334.186
.194
.900a
Residual
48296.409
28
1724.872
Total
49298.969
31
a. Predictors: (Constant), umurkuadrat, indeksmassatubuh, umur
b. Dependent Variable: trigliserida

Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
214.510
108.129
1.984
.057
indeksmassatubuh
-.107
2.166
-.015
-.050
.961
Umur
-1.886
3.951
-.699
-.477
.637
umurkuadrat
.010
.022
.653
.482
.634
a. Dependent Variable: trigliserida
Kita lakukanujiparsial F sepertiberikut (berdasarkanhasil-hasil yang sudahkitalakukandiatas)
ANOVA Tabeluntuk TRIG denganIMTdanUM , UMSQ
Sumber
Df
SS
MS
F
r2
X1
1
601.667
601.667
0.34881
0.900
Regresi X2│X1
1
1.00018
1.00018
0.00058
X3│X1, X2
1
1.66600
1.66600
0.00966
Residual
28
48296.409
1724.872

Total
31
49298.969


Nilai F untukpenambahan independent variabel X3 = 0.00966 <  F 4.02  iniberartihipotesa H0 : β3 = 0 diterimaataugagalditolakartinyapenambahan third order ( X 3) tidaksecarabermaknadapatmemprediksi Y.
Kita bersimpulanbahwa :
a.       Penambahan “ second order” sesuai (fit)  dengannilai r2 = 0.021
b.      Penambahannilai r2 menjadi0.900 pada “ thind order” hanyasebesar 0879 adalahkecil
c.       Kurva yang adacukupditerangkandengan “second order”