SPSS case study

8/18/2019 SPSS case study

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TUGAS UAS STATISTIK DENGAN BANTUAN SPSS dan EXCEL

RAHADIAN RAHMAN PURNAMA

PRA-MM KELAS EKSEKUTIF – ANGKATAN X

Jakarta, 20 Desember 2015

Dosen : Dr. Corry Yohana, MM.


2/18

SOAL 1

DATA RISET

CONSUMPTION

(Y)

INCOME

(X1)

FAMILY MEMBERS

(X2)

8 21 6

9 23 7

11 22 7

13 24 8

7 19 6

14 25 8

16 26 8

18 28 9

14 24 7

10 23 6

1a) Regresi Linier antara variable X1 = Income dan Y = Consumption

1b) Regresi Linier antara variable X2 = Family Members dan Y = Consumption

1c) Regresi Berganda antara variable X1 = Income, X2 = Family Members dan Y = Consumption


3/18

1a) Regresi Linier antara variable X1 = Income dan Y = Consumption

Regression Linear X1 and Y

[DataSet1] E:\Rahadian\MM\Tugas Regresi RAHADIAN.sav

Descriptive Statistics

Mean Std. Deviation N

ConsumptionY 12.0000 3.59011 10

IncomeX1 23.5000 2.54951 10

Correlations

ConsumptionY IncomeX1

Pearson CorrelationConsumptionY 1.000 .947

IncomeX1 .947 1.000

Sig. (1-tailed)ConsumptionY . .000

IncomeX1 .000 .

NConsumptionY 10 10

IncomeX1 10 10


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Variables Entered/Removeda

Model Variables Entered Variables

Removed

Method

1 IncomeX1 . Enter

a. Dependent Variable: ConsumptionY

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics Durbin-Watson

R Square

Change

F Change df1 df2 Sig. F Change

1 .947a .897 .884 1.22474 .897 69.333 1 8 .000 2.130

a. Predictors: (Constant), IncomeX1

b. Dependent Variable: ConsumptionY

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 104.000 1 104.000 69.333 .000

Residual 12.000 8 1.500

Total 116.000 9


b. Predictors: (Constant), IncomeX1


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Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. 95.0% Confidence Interval

for B

Correlations Collinearity Statistics

B Std. Error Beta Lower Bound Upper Bound Zero-

order

Partial Part Tolerance VIF

1(Constant) -19.333 3.783 -5.111 .001 -28.057 -10.610

IncomeX1 1.333 .160 .947 8.327 .000 .964 1.703 .947 .947 .947 1.000 1.000


Coefficient Correlationsa

Model IncomeX1

1

Correlations IncomeX1 1.000

Covariances IncomeX1 .026


Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index Variance Proportions

(Constant) IncomeX1

11 1.995 1.000 .00 .00

2 .005 19.483 1.00 1.00



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Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 6.0000 18.0000 12.0000 3.39935 10

Residual -2.33333 1.33333 .00000 1.15470 10

Std. Predicted Value -1.765 1.765 .000 1.000 10

Std. Residual -1.905 1.089 .000 .943 10


Kesimpulan :

Persamaan Y = -19.333 + 1.333X

R = 0.947 ; R 2 = 0.897, artinya : Pengaruh variable X terhadap

Y = 89.7 %

F hitung = 69.333, Signifikansi = 0.00 < 0.05 (OK)

T hitung = 8.327, Signifikansi = 0.00 < 0.05 (ada pengaruh

yang nyata (signifikan) X terhadap Y

Apabila X1 = 100, maka :

Y = -19.333 + 1.333 (100)

Y = 114


7/18

1b) Regresi Linier antara variable X2 = Family Members dan Y = Consumption

Regression Linear X2 and Y




ConsumptionY 12.0000 3.59011 10

FamilyMembersX2 7.2000 1.03280 10

Correlations

ConsumptionY FamilyMembersX

2

Pearson CorrelationConsumptionY 1.000 .899

FamilyMembersX2 .899 1.000

Sig. (1-tailed)ConsumptionY . .000

FamilyMembersX2 .000 .

N

ConsumptionY 10 10

FamilyMembersX2 10 10


8/18



Removed

Method

1

FamilyMembersX

2b . Enter



Model Summary


Square

Std. Error of the

Estimate


R Square

Change


1 .899

a

.808 .784 1.66771 .808 33.708 1 8 .000 .966

a. Predictors: (Constant), FamilyMembersX2


ANOVAa


1

Regression 93.750 1 93.750 33.708 .000

Residual 22.250 8 2.781

Total 116.000 9


b. Predictors: (Constant), FamilyMembersX2


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Coefficientsa


Coefficients

Standardized

Coefficients

t Sig. 95.0% Confidence

Interval for B

Correlations Collinearity

Statistics

B Std.

Error

Beta Lower

Bound

Upper

Bound

Zero-

order


1(Constant) -10.500 3.911

-

2.685.028 -19.519 -1.481

FamilyMembersX2 3.125 .538 .899 5.806 .000 1.884 4.366 .899 .899 .899 1.000 1.000



Model FamilyMembersX2

1Correlations FamilyMembersX2 1.000

Covariances FamilyMembersX2 .290




(Constant) FamilyMembersX2

11 1.991 1.000 .00 .00

2 .009 14.765 1.00 1.00



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Predicted Value 8.2500 17.6250 12.0000 3.22749 10

Residual -2.37500 2.62500 .00000 1.57233 10


Std. Residual -1.424 1.574 .000 .943 10


Kesimpulan :

Persamaan Y = -10.5 + 3.125X

R = 0.899 ; R 2 = 0.808, artinya : Pengaruh variable X terhadap

Y = 80.8 %

F hitung = 33.708, Signifikansi = 0.00 < 0.05 (OK)

T hitung = 5.806, Signifikansi = 0.00 < 0.05 (ada pengaruh

yang nyata (signifikan) X terhadap Y

Apabila X2 = 50, maka :Y = -10.5 + 3.125 (50)

Y = 145.75


11/18

1c) Regresi Berganda antara variable X1 = Income, X2 = Family Members dan Y = Consumption

Regression Multiple X1, X2 and Y




ConsumptionY 12.0000 3.59011 10

IncomeX1 23.5000 2.54951 10

FamilyMembersX2 7.2000 1.03280 10

Correlations

ConsumptionY IncomeX1 FamilyMembersX

2

Pearson Correlation

ConsumptionY 1.000 .947 .899

IncomeX1 .947 1.000 .886

FamilyMembersX2 .899 .886 1.000

Sig. (1-tailed)

ConsumptionY . .000 .000

IncomeX1 .000 . .000

FamilyMembersX2 .000 .000 .

N

ConsumptionY 10 10 10

IncomeX1 10 10 10

FamilyMembersX2 10 10 10


12/18



Removed

Method

1FamilyMembersX

2, IncomeX1b

. Enter



Model Summaryb


Square

Std. Error of the

Estimate


R Square

Change


1 .956a .913 .889 1.19879 .913 36.859 2 7 .000 2.452

a. Predictors: (Constant), FamilyMembersX2, IncomeX1


ANOVAa


1

Regression 105.940 2 52.970 36.859 .000b

Residual 10.060 7 1.437

Total 116.000 9


b. Predictors: (Constant), FamilyMembersX2, IncomeX1


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Coefficientsa


Coefficients

Standardized

Coefficients

t Sig. 95.0% Confidence Interval

for B

Correlations Collinearity

Statistics

B Std. Error Beta Lower

Bound

Upper

Bound

Zero-

order


1

(Constant) -18.134 3.844 -4.718 .002 -27.224 -9.045

IncomeX1 .985 .338 .700 2.912 .023 .185 1.785 .947 .740 .324 .215 4.657

FamilyMembersX2 .970 .835 .279 1.162 .283 -1.004 2.944 .899 .402 .129 .215 4.657



Model FamilyMembersX2 IncomeX1

1

CorrelationsFamilyMembersX2 1.000 -.886

IncomeX1 -.886 1.000

CovariancesFamilyMembersX2 .697 -.250

IncomeX1 -.250 .114



14/18



(Constant) IncomeX1 FamilyMembersX2

1

1 2.989 1.000 .00 .00 .00

2 .010 17.681 .62 .01 .14

3 .001 45.685 .38 .99 .86




Predicted Value 6.4030 18.1791 12.0000 3.43091 10

Residual -2.31343 1.70149 .00000 1.05723 10


Std. Residual -1.930 1.419 .000 .882 10


Kesimpulan :

Persamaan Y = -18.134 + 0.985X1 + 0.970X2

R = 0.956 ; R 2 = 0.913, artinya : Pengaruh variable X terhadap Y = 91.3 %

Apabila X1 = 100 dan X2 = 50, maka :

Y = -18.134 + 0.985 (100) + 0.970 (50)

Y = 128.866


15/18

SOAL 2

TahunProduksi

(ribu unit)

01. 2

02. 5

03. 9

04. 15

05. 24

06. 37

07. 50

08. 69

09. 82

10. 99

a. Hitunglah nilai trend dengan menggunakan metode least square

b. Hitunglah nilai trend dengan menggunakan metode kuadratis

c. Hitunglah nilai trend dengan menggunakan metode eksponensial

d. Berapa penambahan jumlah angkutan penumpang pada tahun 11


16/18

TahunProduksi (ribu

unit) ( LEAST SQUARE METHOD ) QUADRATIC TREND METHOD METODA TREND EKSPONENSIAL

(Y) kode X Y . X X2 X

2Y X

4 Ln Y X LnY

1 2 -4.50 -9.00 20.25 40.50 410.06 0.69 -3.12

2 5 -3.50 -17.50 12.25 61.25 150.06 1.61 -5.63

3 9 -2.50 -22.50 6.25 56.25 39.06 2.20 -5.49

4 15 -1.50 -22.50 2.25 33.75 5.06 2.71 -4.06

5 24 -0.50 -12.00 0.25 6.00 0.06 3.18 -1.59

6 37 0.50 18.50 0.25 9.25 0.06 3.61 1.81

7 50 1.50 75.00 2.25 112.50 5.06 3.91 5.87

8 69 2.50 172.50 6.25 431.25 39.06 4.23 10.59

9 82 3.50 287.00 12.25 1004.50 150.06 4.41 15.42

10 99 4.50 445.50 20.25 2004.75 410.06 4.60 20.68

Sum 392.00 0.00 915.00 82.50 3760.00 1208.63 31.14 34.46

1a. METODA KUADRAT TERKECIL ( LEAST SQUARE METHOD )

a = 39.20

b = 11.09

Y' = 39.20 + 11.09 X

Untuk Tahun ke 11

x = 5.5

Y' = 100.2

Y’ = a + b X a = ∑Y / n

b = ∑ XY / ∑ X2


17/18

1b. METODA TREND KUADRATIS ( QUADRATIC TREND METHOD )

a = 30.98

b = 11.09

c = 1.00

Y' = -189.42 + 11.09 X + X2

Untuk Tahun ke 11

x = 5.5

Y' = 122.12

Y’ = a + b X + c X2 ( ∑Y ) ( ∑X4 ) -- ( ∑X2Y ) ( ∑X2 )

a = -----------------------------------------------n ( ∑X4 ) -- ( ∑X2 )2

b = ∑XY / ∑X2 n( ∑X2Y ) - ( ∑X2 ) ( ∑Y )

c = -----------------------------------------------n ( ∑X4 ) -- ( ∑X2 )2


18/18

c. METODA TREND EKSPONENSIAL

a= 22.52

b= 0.52

Y' = 22.52 (1 + 0.52 )x

Untuk Tahun ke 11

x = 5.5

Y' = 224.10

Persamaan Trend Peramalan Tahun ke 11

Persamaan Trend Least Square …. Y' = 39.20 + 11.09 X 100.2

Persamaan Kuadratis menjadi ……. Y' = -189.42 + 11.09 X + X2 122.12

Persamaan Trend Eksponensial .. Y' = 22.52 (1 + 0.52 ) x 224.10

-SELESAI-

Y = a ( 1 + b )X Y’ = a ( 1 + b )X

Ln Y’ = Ln a + X Ln ( 1 + b ) a = anti Ln ( ∑ Ln Y ) / n

∑ ( X. Ln Y )

b = anti Ln ------------------ -- 1∑( X )2

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