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


Indeks Prestasi

Jumlah SKS


Indeks Prestasi

Jumlah SKS


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.


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.


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


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


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


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