Pp Regresi
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Transcript of Pp Regresi
REGRESIREGRESIKELOMPOK 4:KELOMPOK 4:
ARMAN FERNANDO. SARMAN FERNANDO. S
DETTI APRIANIDETTI APRIANI
ENI INDRIATIENI INDRIATI
DEFINISI REGRESI# Menurut Sir Francis Galton (1822-1911) Persamaan Regresi :Persamaan matematik yang
memungkinkan peramalan nilai suatu peubah takbebas (dependent variable) dari nilai peubah bebas (independent variable).
Jenis-jenis Persamaan Regresi : a. Regresi Linier : Regresi Linier Sederhana & Regresi Linier Berganda b. Regresi Nonlinierc. Regresi Eksponensial
- Bentuk Umum Regresi Linier Sederhana • Y = a + bX • Y : peubah takbebas • X : peubah bebas • a : konstanta • b : kemiringan
NIM Jumlah SKS Nilai Morfologi tumbuhan Indeks Prestasi
2224082441 21 2 2.6
2224082443 24 3 3.08
2224082447 21 4 2.95
2224082453 21 4 2.95
2224082466 18 3 2.39
2224082472 21 3 2.69
Regression
Descriptive Statistics
2.7767 .26090 6
21.0000 1.89737 6
3.1667 .75277 6
Indeks Prestasi
Jumlah SKS
Nilai Morfologi Tumbuhan
Mean Std. Deviation N
Correlations
1.000 .836 .533
.836 1.000 .000
.533 .000 1.000
. .019 .138
.019 . .500
.138 .500 .
6 6 6
6 6 6
6 6 6
Indeks Prestasi
Jumlah SKS
Nilai Morfologi Tumbuhan
Indeks Prestasi
Jumlah SKS
Nilai Morfologi Tumbuhan
Indeks Prestasi
Jumlah SKS
Nilai Morfologi Tumbuhan
Pearson Correlation
Sig. (1-tailed)
N
IndeksPrestasi Jumlah SKS
Nilai MorfologiTumbuhan
Variables Entered/Removedb
NilaiMorfologiTumbuhan, JumlahSKS
a
. Enter
Model1
VariablesEntered
VariablesRemoved Method
All requested variables entered.a.
Dependent Variable: Indeks Prestasib.
Model Summaryb
.992a .983 .972 .04328 .983 89.327 2 3 .002Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), Nilai Morfologi Tumbuhan, Jumlah SKSa.
Dependent Variable: Indeks Prestasib.
ANOVAb
.335 2 .167 89.327 .002a
.006 3 .002
.340 5
Regression
Residual
Total
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Nilai Morfologi Tumbuhan, Jumlah SKSa.
Dependent Variable: Indeks Prestasib.
Coefficientsa
-.223 .230 -.971 .403
.115 .010 .836 11.272 .001 .836 .988 .836 1.000 1.000
.185 .026 .533 7.183 .006 .533 .972 .533 1.000 1.000
(Constant)
Jumlah SKS
Nilai Morfologi Tumbuhan
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Zero-order Partial Part
Correlations
Tolerance VIF
Collinearity Statistics
Dependent Variable: Indeks Prestasia.
Collinearity Diagnosticsa
2.965 1.000 .00 .00 .01
.032 9.702 .02 .04 .95
.003 30.191 .98 .96 .04
Dimension1
2
3
Model1
EigenvalueCondition
Index (Constant) Jumlah SKSNilai Morfologi
Tumbuhan
Variance Proportions
Dependent Variable: Indeks Prestasia.
Residuals Statisticsa
2.4009 3.0909 2.7767 .25873 6
-1.452 1.214 .000 1.000 6
.018 .036 .030 .007 6
2.4236 3.1136 2.7699 .26826 6
-.05588 .03882 .00000 .03353 6
-1.291 .897 .000 .775 6
-1.423 1.510 .062 1.035 6
-.06786 .11000 .00681 .06460 6
-2.037 2.515 .124 1.495 6
.049 2.549 1.667 1.010 6
.080 1.393 .328 .522 6
.010 .510 .333 .202 6
Predicted Value
Std. Predicted Value
Standard Error ofPredicted Value
Adjusted Predicted Value
Residual
Std. Residual
Stud. Residual
Deleted Residual
Stud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Leverage Value
Minimum Maximum Mean Std. Deviation N
Dependent Variable: Indeks Prestasia.
Charts