TUGAS HALAMAN
221
ESTIMASI MODEL 1 : TRIG =167.677 - 0.792 IMT
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ANOVAb
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Model
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Sum of Squares
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df
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Mean Square
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F
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Sig.
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1
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Regression
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601.667
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1
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601.667
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.371
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.547a
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Residual
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48697.302
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30
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1623.243
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Total
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49298.969
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31
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a.
Predictors: (Constant), indeksmassatubuh
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b.
Dependent Variable: trigliserida
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Coefficientsa
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Model
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Unstandardized Coefficients
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Standardized Coefficients
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t
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Sig.
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B
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Std. Error
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Beta
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||||
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1
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(Constant)
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167.677
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46.066
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3.640
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.001
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indeksmassatubuh
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-.792
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1.300
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-.110
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-.609
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.547
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a.
Dependent Variable: trigliserida
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ESTIMASI MODEL 2 : TRIG = 149.943 - 0.177 UMUR
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ANOVAb
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Model
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Sum of Squares
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Df
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Mean Square
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F
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Sig.
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|
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1
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Regression
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212.189
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1
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212.189
|
.130
|
.721a
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Residual
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49086.780
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30
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1636.226
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Total
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49298.969
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31
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a.
Predictors: (Constant), umur
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b.
Dependent Variable: trigliserida
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Coefficientsa
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||||||
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Model
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Unstandardized Coefficients
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Standardized Coefficients
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t
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Sig.
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||
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B
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Std. Error
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Beta
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||||
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1
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(Constant)
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149.943
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28.605
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5.242
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.000
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umur
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-.177
|
.492
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-.066
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-.360
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.721
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a.
Dependent Variable: trigliserida
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ESTIMASI MODEL 3 : TRIG = 142.230 + 0.000 UMUR KUADRAT
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ANOVAb
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||||||
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Model
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Sum of Squares
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df
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Mean Square
|
F
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Sig.
|
|
|
1
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Regression
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85.385
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1
|
85.385
|
.052
|
.821a
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Residual
|
49213.584
|
30
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1640.453
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Total
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49298.969
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31
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a.
Predictors: (Constant), umurkuadrat
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|||
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b.
Dependent Variable: trigliserida
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Coefficientsa
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||||||
|
Model
|
Unstandardized Coefficients
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Standardized Coefficients
|
t
|
Sig.
|
||
|
B
|
Std. Error
|
Beta
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||||
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1
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(Constant)
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142.230
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12.226
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11.634
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.000
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umurkuadrat
|
.000
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.003
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-.042
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-.228
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.821
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a.
Dependent Variable: trigliserida
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ESTIMASI MODEL 4 :167.688 - 0.784 IMT - 0.005 UMUR
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ANOVAb
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||||||
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Model
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Sum of Squares
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df
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Mean Square
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F
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Sig.
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|
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1
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Regression
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601.777
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2
|
300.889
|
.179
|
.837a
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Residual
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48697.191
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29
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1679.213
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Total
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49298.969
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31
|
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a.
Predictors: (Constant), umur, indeksmassatubuh
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b.
Dependent Variable: trigliserida
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|||
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Coefficientsa
|
||||||
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
|
B
|
Std. Error
|
Beta
|
||||
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1
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(Constant)
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167.688
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46.872
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3.578
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.001
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indeksmassatubuh
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-.784
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1.628
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-.109
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-.482
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.634
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Umur
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-.005
|
.613
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-.002
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-.008
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.994
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a.
Dependent Variable: trigliserida
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ESTIMASI MODEL 5 :168.623 - 0.841 IMT + 0.000 UMUR KUADRAT
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ANOVAb
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Model
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Sum of Squares
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df
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Mean Square
|
F
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Sig.
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1
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Regression
|
609.613
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2
|
304.806
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.182
|
.835a
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Residual
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48689.356
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29
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1678.943
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Total
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49298.969
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31
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a.
Predictors: (Constant), umurkuadrat, indeksmassatubuh
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||||
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b.
Dependent Variable: trigliserida
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|||
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Coefficientsa
|
||||||
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
|
B
|
Std. Error
|
Beta
|
||||
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1
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(Constant)
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168.623
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48.827
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3.453
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.002
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indeksmassatubuh
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-.841
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1.505
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-.117
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-.559
|
.581
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umurkuadrat
|
.000
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.003
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.014
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.069
|
.946
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a.
Dependent Variable: trigliserida
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ESTIMASI MODEL 6 :214.510 - 0.107 IMT - 1.886 UMUR + 0.010 UMUR KUADRAT
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ANOVAb
|
||||||
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Model
|
Sum of Squares
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df
|
Mean Square
|
F
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Sig.
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|
1
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Regression
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1002.559
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3
|
334.186
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.194
|
.900a
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Residual
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48296.409
|
28
|
1724.872
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Total
|
49298.969
|
31
|
|
|
|
|
|
a.
Predictors: (Constant), umurkuadrat, indeksmassatubuh, umur
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|||||
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b.
Dependent Variable: trigliserida
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|||
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Coefficientsa
|
||||||
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
||
|
B
|
Std. Error
|
Beta
|
||||
|
1
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(Constant)
|
214.510
|
108.129
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|
1.984
|
.057
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|
indeksmassatubuh
|
-.107
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2.166
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-.015
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-.050
|
.961
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Umur
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-1.886
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3.951
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-.699
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-.477
|
.637
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umurkuadrat
|
.010
|
.022
|
.653
|
.482
|
.634
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a.
Dependent Variable: trigliserida
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Kita
lakukanujiparsial F sepertiberikut (berdasarkanhasil-hasil yang
sudahkitalakukandiatas)
ANOVA Tabeluntuk
TRIG denganIMTdanUM , UMSQ
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Sumber
|
Df
|
SS
|
MS
|
F
|
r2
|
|
X1
|
1
|
601.667
|
601.667
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0.34881
|
0.900
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Regresi X2│X1
|
1
|
1.00018
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1.00018
|
0.00058
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X3│X1, X2
|
1
|
1.66600
|
1.66600
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0.00966
|
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Residual
|
28
|
48296.409
|
1724.872
|
||
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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”
