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SPSS untuk Peramalan Permintaan Produk (Forecasting)

Input:


Output:
Regression

Variables Entered/Removed(b)
















Model
Variables Entered
Variables Removed
Method
1x5, x4, x3, x2, x1(a)
-
Enter


a. All requested variables entered.
b. Dependent Variable: y

Model Summary(b)


















Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
,754(a)
,569
,329
24,72143


a. Predictors: (Constant), x5, x4, x3, x2, x1
b. Dependent Variable: y

ANOVA(b)








































Model
-
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
7256,590
5
1451,318
2,375
,123(a)

Residual
5500,343
9
611,149
-
-

Total
12756,933
14
-
-
-


a. Predictors: (Constant), x5, x4, x3, x2, x1
b. Dependent Variable: y

Coefficients(a)
























































































Model

Unstandardized Coefficients
Standardized Coefficients
T
Sig.
Collinearity Statistics
B
Std. Err.
Beta
Tolerance
VIF
1(Const.)
356,208
80,531
-
4,423
,002
-
-

x1
2,052
2,066
,658
,993
,347
,109
9,162

x2
-,393
,276
-,496
-1,423
,188
,395
2,534

x3
-11,963
21,217
-,201
-,564
,587
,377
2,651

x4
1,966
4,696
,253
,419
,685
,132
7,592

x5
-8,576
8,273
-,262
-1,037
,327
,750
1,333


a. Dependent Variable: y

Collinearity Diagnostics(a)





























































































Model
Dimension
Eigen Value
Condition Index
Variance Proportions
(Constant)
x1
x2
x3
x4
x5
11
5,774
1,000
,00
,00
,00
,00
,00
,00

2
,120
6,944
,00
,01
,01
,04
,01
,13

3
,076
8,735
,00
,00
,00
,15
,01
,26

4
,017
18,423
,01
,05
,55
,05
,06
,02

5
,011
22,827
,28
,13
,07
,01
,08
,54

6
,002
48,526
,71
,81
,37
,75
,84
,05


a. Dependent Variable: y

Residuals Statistics(a)













































































































Minimum
Maximum
Mean
Std. Deviation
N
Predicted Value
292,9666
373,5478
333,0667
22,76681
15
Std. Predicted Value
-1,761
1,778
,000
1,000
15
Standard Error of Predicted Value
8,653
21,728
15,238
3,625
15
Adjusted Predicted Value
287,3006
400,1336
334,8739
28,12028
15
Residual
-38,46859
32,12692
,00000
19,82124
15
Std. Residual
-1,556
1,300
,000
,802
15
Stud. Residual
-2,355
1,588
-,028
1,067
15
Deleted Residual
-88,13363
47,99394
-1,80723
35,87600
15
Stud. Deleted Residual
-3,585
1,765
-,124
1,329
15
Mahal. Distance
,782
9,882
4,667
2,411
15
Cook's Distance
,000
1,194
,151
,302
15
Centered Leverage Value
,056
,706
,333
,172
15


a. Dependent Variable: y

Histogram - Dependent Variable: y

Regression Standardized Residual

Normal P-Plot of Regression Standardized Residual - Dependent Variable: y

Observed Cum Prob

Dari print out di atas, model untuk menduga hasil penjualan produk berdasarkan variabel bebas (dana promosi, luas fasilitas outlet, laju pertumbuhan penduduk per tahun, banyaknya pesaing, serta rata-rata pendapatan penduduk per tahun) dapat dirumuskan sebagai berikut:
Y = 0,658 X1 - 0,496 X2 - 0,201 X3 + 0,253 X4 - 0,262 X5 + E


Scatterplot - Dependent Variable: y

Regression Standardized Predicted Value

Model tersebut bukan model yang terbaik karena:
1. Nilai R2 terlalu rendah = 0,569 yang artinya variasi perubahan pada Y hanya dapat dijelaskan sebesar 56,9% dari perubahan variabel bebasnya. Sisanya sebesar 43,1% tidak dapat dijelaskan.
2. Nilai Sig. dari F (0,123) lebih besar dari 0,05 yang artinya variabel-variabel bebasnya secara simultan TIDAK signifikan mempengaruhi variabel terikat (Y) pada level of significance 5%.
Nilai Sig. dari masing-masing t juga lebih besar dari 0,05 yang artinya variabel-variabel bebasnya secara parsial TIDAK signifikan mempengaruhi variabel terikat (Y) pada level of significance 5%.

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“SPSS untuk Peramalan Permintaan Produk (Forecasting)”

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