ANALISIS REGRESI
Untuk memenuhi tugas terstruktur mata kuliah Aplikom Statistik yang
dibimbing oleh bapak Bayu Ilham Pradana, SE, MM.
 |
Disusun oleh :
Lilik Choirotul
Mafula
115020200111111
UNIVERSITAS BRAWIJAYA
FAKULTAS EKONOMI DAN BISNIS
JURUSAN
MANAJEMEN
MALANG
2012
SOAL
Data
View
|
penjualan
|
promosi
|
outlet
|
|
205
|
26
|
159
|
|
206
|
28
|
164
|
|
254
|
35
|
198
|
|
246
|
31
|
184
|
|
201
|
21
|
150
|
|
291
|
49
|
208
|
|
234
|
30
|
184
|
|
209
|
30
|
154
|
|
204
|
24
|
149
|
|
216
|
31
|
175
|
|
245
|
32
|
192
|
|
286
|
47
|
201
|
|
312
|
54
|
248
|
|
265
|
40
|
166
|
|
322
|
42
|
287
|
Variable
view
|
Name
|
Type
|
Width
|
Decimals
|
Label
|
Values
|
Missing
|
Colums
|
Align
|
measure
|
|
Penjualan
|
Numeric
|
8
|
0
|
|
None
|
None
|
8
|
Right
|
Scale
|
|
Promosi
|
Numeric
|
8
|
0
|
|
None
|
None
|
8
|
Right
|
Scale
|
|
outlet
|
Numeric
|
8
|
0
|
|
None
|
None
|
8
|
Right
|
scale
|
HASIL OUTPUT SPSS
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT penjualan
/METHOD=ENTER promosi outlet.
Regression
|
Notes
|
||
|
Output Created
|
27-Nov-2012
13:22:57
|
|
|
Comments
|
|
|
|
Input
|
Data
|
D:\semester 3\AP. STATISTIK\tugas 29 nov.sav
|
|
Active Dataset
|
DataSet1
|
|
|
Filter
|
<none>
|
|
|
Weight
|
<none>
|
|
|
Split File
|
<none>
|
|
|
N of Rows in Working Data File
|
15
|
|
|
Missing Value Handling
|
Definition of Missing
|
User-defined missing values are treated as missing.
|
|
Cases Used
|
Statistics are based on cases with no missing values for any
variable used.
|
|
|
Syntax
|
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT penjualan
/METHOD=ENTER promosi
outlet.
|
|
|
Resources
|
Processor Time
|
00:00:00,047
|
|
Elapsed Time
|
00:00:00,054
|
|
|
Memory Required
|
1636
bytes
|
|
|
Additional Memory Required for Residual Plots
|
0 bytes
|
|
[DataSet1] D:\semester 3\AP.
STATISTIK\tugas 29 nov.sav
|
Variables
Entered/Removedb
|
||||
|
Model
|
Variables
Entered
|
Variables
Removed
|
Method
|
|
|
dimension0
|
1
|
outlet, promosia
|
.
|
Enter
|
|
a. All requested variables entered.
|
||||
|
b. Dependent Variable: penjualan
|
||||
|
Model
Summary
|
|||||
|
Model
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
|
|
dimension0
|
1
|
,976a
|
,952
|
,944
|
9,757
|
|
a.
Predictors: (Constant), outlet, promosi
|
|||||
|
ANOVAb
|
||||||
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
|
1
|
Regression
|
22521,299
|
2
|
11260,649
|
118,294
|
,000a
|
|
Residual
|
1142,301
|
12
|
95,192
|
|
|
|
|
Total
|
23663,600
|
14
|
|
|
|
|
|
a.
Predictors: (Constant), outlet, promosi
|
||||||
|
b. Dependent Variable:
penjualan
|
||||||
|
Coefficientsa
|
||||||
|
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
|
B
|
Std.
Error
|
Beta
|
||||
|
1
|
(Constant)
|
64,639
|
13,112
|
|
4,930
|
,000
|
|
promosi
|
2,342
|
,398
|
,551
|
5,892
|
,000
|
|
|
outlet
|
,535
|
,101
|
,496
|
5,297
|
,000
|
|
|
a. Dependent Variable: penjualan
|
||||||
* Curve Estimation.
TSET NEWVAR=NONE.
CURVEFIT
/VARIABLES=penjualan WITH promosi
/CONSTANT
/MODEL=LINEAR EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
Curve Fit
|
Notes
|
||
|
Output Created
|
27-Nov-2012
13:24:24
|
|
|
Comments
|
|
|
|
Input
|
Data
|
D:\semester 3\AP. STATISTIK\tugas 29 nov.sav
|
|
Active Dataset
|
DataSet1
|
|
|
Filter
|
<none>
|
|
|
Weight
|
<none>
|
|
|
Split File
|
<none>
|
|
|
N of Rows in Working Data File
|
15
|
|
|
Missing Value Handling
|
Definition of Missing
|
User-defined missing values are treated as missing.
|
|
Cases Used
|
Cases with a missing value in any variable are not used in the
analysis.
|
|
|
Syntax
|
CURVEFIT
/VARIABLES=penjualan
WITH promosi
/CONSTANT
/MODEL=LINEAR
EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
|
|
|
Resources
|
Processor Time
|
00:00:01,077
|
|
Elapsed Time
|
00:00:01,052
|
|
|
Use
|
From
|
First observation
|
|
To
|
Last observation
|
|
|
Predict
|
From
|
First Observation following the use period
|
|
To
|
Last observation
|
|
|
Time Series Settings (TSET)
|
Amount of Output
|
PRINT = DEFAULT
|
|
Saving New Variables
|
NEWVAR = NONE
|
|
|
Maximum Number of Lags in Autocorrelation or Partial
Autocorrelation Plots
|
MXAUTO = 16
|
|
|
Maximum Number of Lags Per Cross-Correlation Plots
|
MXCROSS = 7
|
|
|
Maximum Number of New Variables Generated Per Procedure
|
MXNEWVAR = 60
|
|
|
Maximum Number of New Cases Per Procedure
|
MXPREDICT = 1000
|
|
|
Treatment of User-Missing Values
|
MISSING = EXCLUDE
|
|
|
Confidence Interval Percentage Value
|
CIN = 95
|
|
|
Tolerance for Entering Variables in Regression Equations
|
TOLER = ,0001
|
|
|
Maximum Iterative Parameter Change
|
CNVERGE = ,001
|
|
|
Method of Calculating Std. Errors for Autocorrelations
|
ACFSE = IND
|
|
|
Length of Seasonal Period
|
Unspecified
|
|
|
Variable Whose Values Label Observations in Plots
|
Unspecified
|
|
|
Equations Include
|
CONSTANT
|
|
[DataSet1] D:\semester 3\AP.
STATISTIK\tugas 29 nov.sav
|
Model
Description
|
||
|
Model Name
|
MOD_5
|
|
|
Dependent Variable
|
1
|
penjualan
|
|
Equation
|
1
|
Linear
|
|
2
|
Exponentiala
|
|
|
Independent Variable
|
promosi
|
|
|
Constant
|
Included
|
|
|
Variable Whose Values Label Observations in Plots
|
Unspecified
|
|
|
a. The model requires all non-missing values to be positive.
|
||
|
Case
Processing Summary
|
|
|
|
N
|
|
Total Cases
|
15
|
|
Excluded Casesa
|
0
|
|
Forecasted Cases
|
0
|
|
Newly Created Cases
|
0
|
|
a. Cases
with a missing value in any variable are excluded from the analysis.
|
|
|
Variable
Processing Summary
|
|||
|
|
Variables
|
||
|
Dependent
|
Independent
|
||
|
penjualan
|
promosi
|
||
|
Number of Positive Values
|
15
|
15
|
|
|
Number of Zeros
|
0
|
0
|
|
|
Number of Negative Values
|
0
|
0
|
|
|
Number of Missing Values
|
User-Missing
|
0
|
0
|
|
System-Missing
|
0
|
0
|
|
Penjualan
Linear
|
Model Summary
|
|||||||||||||||
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
||||||||||||
|
,916
|
,839
|
,826
|
17,127
|
||||||||||||
|
The independent variable is promosi.
|
|||||||||||||||
|
ANOVA
|
|||||||||||||||
|
|
Sum of
Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
||||||||||
|
Regression
|
19850,334
|
1
|
19850,334
|
67,673
|
,000
|
||||||||||
|
Residual
|
3813,266
|
13
|
293,328
|
|
|
||||||||||
|
Total
|
23663,600
|
14
|
|
|
|
||||||||||
|
The independent variable is promosi.
|
|||||||||||||||
|
Coefficients
|
|||||||||||||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|||||||||||
|
B
|
Std.
Error
|
Beta
|
|||||||||||||
|
promosi
|
3,891
|
,473
|
,916
|
8,226
|
,000
|
||||||||||
|
(Constant)
|
111,523
|
16,982
|
|
6,567
|
,000
|
||||||||||
Exponential
|
Model Summary
|
|||||||||||||||
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
||||||||||||
|
,918
|
,842
|
,830
|
,067
|
||||||||||||
|
The independent variable is promosi.
|
|||||||||||||||
|
ANOVA
|
|||||||||||||||
|
|
Sum of
Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
||||||||||
|
Regression
|
,313
|
1
|
,313
|
69,257
|
,000
|
||||||||||
|
Residual
|
,059
|
13
|
,005
|
|
|
||||||||||
|
Total
|
,371
|
14
|
|
|
|
||||||||||
|
The independent variable is promosi.
|
|||||||||||||||
|
Coefficients
|
|||||||||||||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|||||||||||
|
B
|
Std.
Error
|
Beta
|
|||||||||||||
|
promosi
|
,015
|
,002
|
,918
|
8,322
|
,000
|
||||||||||
|
(Constant)
|
142,475
|
9,490
|
|
15,013
|
,000
|
||||||||||
|
The dependent variable is ln(penjualan).
|
|||||||||||||||
* Curve Estimation.
TSET NEWVAR=NONE.
CURVEFIT
/VARIABLES=penjualan WITH outlet
/CONSTANT
/MODEL=LINEAR EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
Curve Fit
|
Notes
|
||
|
Output Created
|
27-Nov-2012
13:24:45
|
|
|
Comments
|
|
|
|
Input
|
Data
|
D:\semester 3\AP. STATISTIK\tugas 29 nov.sav
|
|
Active Dataset
|
DataSet1
|
|
|
Filter
|
<none>
|
|
|
Weight
|
<none>
|
|
|
Split File
|
<none>
|
|
|
N of Rows in Working Data File
|
15
|
|
|
Missing Value Handling
|
Definition of Missing
|
User-defined missing values are treated as missing.
|
|
Cases Used
|
Cases with a missing value in any variable are not used in the
analysis.
|
|
|
Syntax
|
CURVEFIT
/VARIABLES=penjualan
WITH outlet
/CONSTANT
/MODEL=LINEAR
EXPONENTIAL
/PRINT ANOVA
/PLOT FIT.
|
|
|
Resources
|
Processor Time
|
00:00:01,014
|
|
Elapsed Time
|
00:00:00,982
|
|
|
Use
|
From
|
First observation
|
|
To
|
Last observation
|
|
|
Predict
|
From
|
First Observation following the use period
|
|
To
|
Last observation
|
|
|
Time Series Settings (TSET)
|
Amount of Output
|
PRINT = DEFAULT
|
|
Saving New Variables
|
NEWVAR = NONE
|
|
|
Maximum Number of Lags in Autocorrelation or Partial
Autocorrelation Plots
|
MXAUTO = 16
|
|
|
Maximum Number of Lags Per Cross-Correlation Plots
|
MXCROSS = 7
|
|
|
Maximum Number of New Variables Generated Per Procedure
|
MXNEWVAR = 60
|
|
|
Maximum Number of New Cases Per Procedure
|
MXPREDICT = 1000
|
|
|
Treatment of User-Missing Values
|
MISSING = EXCLUDE
|
|
|
Confidence Interval Percentage Value
|
CIN = 95
|
|
|
Tolerance for Entering Variables in Regression Equations
|
TOLER = ,0001
|
|
|
Maximum Iterative Parameter Change
|
CNVERGE = ,001
|
|
|
Method of Calculating Std. Errors for Autocorrelations
|
ACFSE = IND
|
|
|
Length of Seasonal Period
|
Unspecified
|
|
|
Variable Whose Values Label Observations in Plots
|
Unspecified
|
|
|
Equations Include
|
CONSTANT
|
|
[DataSet1] D:\semester 3\AP.
STATISTIK\tugas 29 nov.sav
|
Model
Description
|
||||||||
|
Model Name
|
MOD_6
|
|||||||
|
Dependent Variable
|
1
|
penjualan
|
||||||
|
Equation
|
1
|
Linear
|
||||||
|
2
|
Exponentiala
|
|||||||
|
Independent Variable
|
outlet
|
|||||||
|
Constant
|
Included
|
|||||||
|
Variable Whose Values Label Observations in Plots
|
Unspecified
|
|||||||
|
a. The model requires all non-missing values to be positive.
|
||||||||
|
Case
Processing Summary
|
||||||||
|
|
N
|
|||||||
|
Total Cases
|
15
|
|||||||
|
Excluded Casesa
|
0
|
|||||||
|
Forecasted Cases
|
0
|
|||||||
|
Newly Created Cases
|
0
|
|||||||
|
a. Cases with a missing value in any variable are excluded from
the analysis.
|
||||||||
|
Variable
Processing Summary
|
||||||||
|
|
Variables
|
|||||||
|
Dependent
|
Independent
|
|||||||
|
penjualan
|
outlet
|
|||||||
|
Number of Positive Values
|
15
|
15
|
||||||
|
Number of Zeros
|
0
|
0
|
||||||
|
Number of Negative Values
|
0
|
0
|
||||||
|
Number of Missing Values
|
User-Missing
|
0
|
0
|
|||||
|
System-Missing
|
0
|
0
|
||||||
penjualan
Linear
|
Model
Summary
|
|||||||||||||||
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
||||||||||||
|
,901
|
,812
|
,798
|
18,495
|
||||||||||||
|
The independent variable is outlet.
|
|||||||||||||||
|
ANOVA
|
|||||||||||||||
|
|
Sum of
Squares
|
Df
|
Mean
Square
|
F
|
Sig.
|
||||||||||
|
Regression
|
19216,954
|
1
|
19216,954
|
56,182
|
,000
|
||||||||||
|
Residual
|
4446,646
|
13
|
342,050
|
|
|
||||||||||
|
Total
|
23663,600
|
14
|
|
|
|
||||||||||
|
The independent variable is outlet.
|
|||||||||||||||
|
Coefficients
|
|||||||||||||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|||||||||||
|
B
|
Std.
Error
|
Beta
|
|||||||||||||
|
outlet
|
,973
|
,130
|
,901
|
7,495
|
,000
|
||||||||||
|
(Constant)
|
63,589
|
24,853
|
|
2,559
|
,024
|
||||||||||
Exponential
|
Model
Summary
|
|||||||||||||||
|
R
|
R
Square
|
Adjusted
R Square
|
Std.
Error of the Estimate
|
||||||||||||
|
,885
|
,783
|
,767
|
,079
|
||||||||||||
|
The independent variable is outlet.
|
|||||||||||||||
|
ANOVA
|
|||||||||||||||
|
|
Sum of
Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
||||||||||
|
Regression
|
,291
|
1
|
,291
|
46,968
|
,000
|
||||||||||
|
Residual
|
,080
|
13
|
,006
|
|
|
||||||||||
|
Total
|
,371
|
14
|
|
|
|
||||||||||
|
The independent variable is outlet.
|
|||||||||||||||
|
Coefficients
|
|||||||||||||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|||||||||||
|
B
|
Std.
Error
|
Beta
|
|||||||||||||
|
outlet
|
,004
|
,001
|
,885
|
6,853
|
,000
|
||||||||||
|
(Constant)
|
119,496
|
12,634
|
|
9,458
|
,000
|
||||||||||
|
The dependent variable is ln(penjualan).
|
|||||||||||||||
HASIL ANALISIS
1.
Membuktikan
Hipotesis
a.
H1
: Meningkatkan aktivitas pemasaran (promosi dan outlet) dapat meningkatkan
penjualan. Berdasarkan tabel hasil regresi tersebut hipotesis profesor X
terbukti
|
ANOVAb
|
||||||
|
Model
|
Sum
of Squares
|
df
|
Mean
Square
|
F
|
Sig.
|
|
|
1
|
Regression
|
22521,299
|
2
|
11260,649
|
118,294
|
,000a
|
|
Residual
|
1142,301
|
12
|
95,192
|
|
|
|
|
Total
|
23663,600
|
14
|
|
|
|
|
|
a.
Predictors: (Constant), outlet, promosi
|
||||||
|
b.
Dependent Variable: penjualan
|
||||||
Dengan menggunakan uji F dapat dilihat
bahwa nilai signifikan antara penjualan, outlet dan promosi ternyata lebih kecil
dari 0,05. Hal tersebut berarti terdapat hubungan yang signifikan antara
aktivitas pemasaran (promosi dan outlet) pada penjualan suatu barang.
b. H2
: Meningkatnya promosi dapat meningkatkan penjualan
Hasil perhitungan menggunakan SPSS melalui uji t menunjukkan
|
Coefficients
|
|||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|
|
B
|
Std. Error
|
Beta
|
|||
|
promosi
|
,015
|
,002
|
,918
|
8,322
|
,000
|
|
(Constant)
|
142,475
|
9,490
|
|
15,013
|
,000
|
|
The dependent variable is
ln(penjualan).
|
|||||
Berdasarkan data tersebut
hipotesis kedua dari professor X terbukti ada hubungan yang signifikan antara
penjualan dengan promosi, hal tersebut terlihat dari nilai signifikan yang
kurang dari 0,05. Yang berarti meningkatknaya promosi dapat pula meningkatkan
jumlah penjualan.
|
c. H3
: Meningkatnya jumlah outlet dapat meningkatkan penjualan
|
Coefficients
|
|||||
|
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|
|
B
|
Std.
Error
|
Beta
|
|||
|
outlet
|
,973
|
,130
|
,901
|
7,495
|
,000
|
|
(Constant)
|
63,589
|
24,853
|
|
2,559
|
,024
|
Berdasarkan data
tersebut hipotesis ketiga dari professor X terbukti ada hubungan yang
signifikan antara penjualan dengan outlet, hal tersebut terlihat dari nilai
signifikan yang kurang dari 0,05. Yang berarti meningkatnya outlet dapat pula
meningkatkan jumlah penjualan.
...
|
2.
Persamaan regresi
|
Coefficientsa
|
||||||
|
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
||
|
B
|
Std.
Error
|
Beta
|
||||
|
1
|
(Constant)
|
64,639
|
13,112
|
|
4,930
|
,000
|
|
promosi
|
2,342
|
,398
|
,551
|
5,892
|
,000
|
|
|
outlet
|
,535
|
,101
|
,496
|
5,297
|
,000
|
|
|
a.
Dependent Variable: penjualan
|
||||||
Dikarenakana
dalam penelitian ini menggunakan dua satuan pengukuran yang berbeda yaitu
satuan unit dan rupiah maka untuk membuat persamaan regresi dalam penelitian
ini menggunakan standardized coefficients beta.
Y
= 0,551.X1+0,496.X2
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