LAMPIRAN 2 Persamaan dengan Metode Kuadrat Terkecil dan Mendeteksi Pencilan dengan MINITAB

LAMPIRAN 1

Membangkitkan Data dengan Program R

LAMPIRAN 2

Persamaan dengan Metode Kuadrat Terkecil dan Mendeteksi Pencilan

dengan MINITAB

Data\MINITAB\Data1MPJ.MPJ' MTB > Name c53 "DFIT3" MTB > Regress 'Y' 1 'X'; SUBC> DFits 'DFIT3'; SUBC> Constant; SUBC> Brief 2.

Regression Analysis: Y versus X

The regression equation is Y = - 4,55 + 1,41 X

Data\MINITAB\Data2MPJ.MPJ' MTB > Name c37 "DFIT2" MTB > Regress 'Y' 1 'X'; SUBC> DFits 'DFIT2'; SUBC> Constant; SUBC> Brief 2.

Regression Analysis: Y versus X

Y = 1,50 + 0,805 X

Data\MINITAB\Data3.1MPJ.MPJ' MTB > Name c47 "DFIT1" MTB > Regress 'Y' 1 'X'; SUBC> DFits 'DFIT1'; SUBC> Constant; SUBC> Brief 2.

Regression Analysis: Y versus X

The regression equation is Y = - 1,97 + 1,21 X

Data\MINITAB\Data4MPJ.MPJ' MTB > Name c49 "DFIT2" MTB > Regress 'Y' 1 'X'; SUBC> DFits 'DFIT2'; SUBC> Constant; SUBC> Brief 2.

Regression Analysis: Y versus X

The regression equation is Y = 0,46 + 0,982 X

LAMPIRAN 3

Persamaan Penduga-LTS dengan Metode Kuadrat Terkecil

MTB > Regress 'Ylt' 1 'Xlt'; SUBC> Constant;

SUBC> Brief 2.

Regression Analysis: Ylt versus Xlt

The regression equation is Ylt = - 3,92 + 1,32 Xlt

MTB > Regress 'Ylt' 1 'Xlt'; SUBC> Constant;

SUBC> Brief 2.

Regression Analysis: Ylt versus Xlt

The regression equation is Ylt = 2,91 + 0,701 Xlt

MTB > Regress 'Ylt' 1 'Xlt'; SUBC> Constant;

Regression Analysis: Ylt versus Xlt

The regression equation is Ylt = - 1,25 + 1,15 Xlt

MTB > Regress 'Ylt' 1 'Xlt'; SUBC> Constant;

SUBC> Brief 2.

Regression Analysis: Ylt versus Xlt

The regression equation is Ylt = - 2,63 + 1,27 Xlt

LAMPIRAN 4

Program Macro MINITAB Regresi Robust dengan Pembobot Fungsi Huber

(dengan r=1)

macro

robust1 X.1 Y

mconstant n s k2 r k1 iter

mcolumn X.1 Y b e eb pseb w b0 b1

mmatrix

let n=count(Y)

regres Y 1 X.1;

coef b;

residual e;

constant;

brief 0.

let b0(1)=b(1)

let b1(1)=b(2)

let iter=15

DO k2=2:iter

let s=1.5*median(abs(e))

let eb=e/s

DO k1=1:n

IF abs(eb(k1))<=r

let pseb(k1)=eb(k1)

ELSEIF eb(k1)>r

let pseb(k1)=r

ELSE

let pseb(k1)=-r

ENDIF

ENDDO

print k2

let w=pseb/eb

print w

regres Y 1 X.1;

weights w;

coef b;

residual e;

constant;

brief 0.

let b0(k2)=b(1)

let b1(k2)=b(2)

ENDDO

print b0 b1

ENDMACRO

Lampiran 5

Hasil Output Program macro MINITAB Data 1, Data 2, Data 3, dan Data 4

Data\MINITAB\Data1MPJ.MPJ' MTB > %estimasiM.txt C1 C2

Executing from file: estimasiM.txt

Data Display

k2 2,00000

Data Display

7 -2,31699 1,19616 MTB > %estimasiM.txt C1 C2

0,45532 0,40779 1,00000 1,00000 1,00000 0,90326 MTB > %estimasiM.txt C1 C2

Data Display

Row b0 b1 1 0,46201 0,98245 2 -1,72584 1,17872 3 -2,51533 1,25312 4 -2,68421 1,26896 5 -2,71469 1,27167 6 -2,72096 1,27221 7 -2,72234 1,27233 8 -2,72266 1,27235 9 -2,72274 1,27236 10 -2,72275 1,27236 11 -2,72276 1,27236 12 -2,72276 1,27236 13 -2,72276 1,27236 14 -2,72276 1,27236 15 -2,72276 1,27236

Lampiran 6

Persamaan Penduga-S Data 1, Data 2, Data 3 dan Data 4 dengan R

Lampiran 7

Perulangan Data Simulasi

Data

ke-

Letak Pencilan di Ujung Bawah

Data 1.1

Data 1.2

Data 1.3

Data 1.4

Letak Pencilan di Tengah Bawah

Data

ke-

Letak Pencilan di Tengah Bawah

Data 2.1

Data 2.2

Data 2.3

Data 2.4

Letak Pencilan di Ujung Atas

Data 3.1

Data 3.2

Data 3.3

Data 3.4

Letak Pencilan di Tengah Atas