In [25]:

    

library(ggplot2)
library(MASS)
library(reshape2)
library(corrplot)
library(plyr)
library(mgcv)
library(sm)
library(vars)
library(lattice)
library(R2HTML)
library(knitr)
library(IRkernel)
options(repr.plot.width = 7)
options(repr.plot.height = 5)


    

    




Loading required package: nlme
This is mgcv 1.8-9. For overview type 'help("mgcv-package")'.
Package 'sm', version 2.2-5.4: type help(sm) for summary information

Attaching package: ‘sm’

The following object is masked from ‘package:MASS’:

    muscle

Loading required package: strucchange
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: urca
Loading required package: lmtest

In [26]:

    

DataOutback <-read.csv("Suharti.csv", header=TRUE, sep=",") 
DataOutback


    

    
Out[26]:





RRS
TampilanS
MenuS
PackagingS
WifiS
PembayaranS
PelayananS

	
1
5
4
4
4
4
4
4
	
2
3
3
3
3
3
4
4
	
3
5
3
4
4
2
5
4
	
4
4
4
3
3
3
4
4
	
5
2
1
1
1
2
3
2
	
6
4
3
4
4
4
4
4
	
7
3
4
4
4
4
4
4
	
8
4
3
2
3
2
4
3
	
9
3
3
3
4
3
4
4
	
10
3
3
4
4
3
4
4
	
11
5
4
3
3
3
4
4
	
12
4
4
3
3
2
4
4
	
13
5
3
3
4
3
5
4
	
14
3
2
2
1
1
4
4
	
15
4
4
4
4
4
4
4
	
16
4
3
3
5
5
5
5
	
17
3
4
4
4
4
4
4
	
18
3
3
2
4
3
4
4
	
19
1
3
3
2
2
2
3
	
20
5
2
2
4
3
3
3
	
21
4
3
3
4
3
4
4
	
22
4
4
2
3
2
5
4
	
23
3
4
4
4
3
4
4
	
24
3
3
2
2
2
4
4
	
25
5
3
4
4
3
4
4
	
26
3
3
3
3
3
4
4
	
27
3
2
2
4
3
4
4
	
28
3
3
3
4
3
4
3
	
29
2
3
2
2
2
2
3
	
30
3
3
3
3
2
3
4
	
31
4
3
2
2
2
4
4
	
32
4
4
4
4
2
3
3
	
33
2
2
2
3
1
4
4
	
34
4
4
3
3
4
4
3
	
35
4
4
4
4
2
4
3
	
36
4
4
4
3
2
3
3
	
37
4
4
5
4
4
3
4
	
38
3
4
3
2
2
3
4
	
39
5
4
3
3
3
4
4
	
40
3
4
3
4
3
5
4
	
41
3
3
4
4
3
4
4
	
42
3
3
3
3
3
3
3
	
43
4
4
3
4
3
4
4
	
44
4
3
2
4
2
4
4
	
45
3
4
4
4
3
4
4
	
46
4
3
3
3
2
4
4
	
47
1
2
3
3
2
3
2
	
48
4
4
4
4
2
4
4
	
49
3
3
2
2
2
3
4
	
50
4
3
3
3
3
4
4
	
51
3
3
3
4
3
4
4
	
52
4
3
4
4
4
4
4
	
53
4
3
3
3
3
4
4
	
54
4
4
4
3
2
4
3
	
55
3
4
4
4
2
3
4
	
56
4
4
3
3
3
4
4
	
57
4
3
3
3
2
4
4
	
58
3
4
4
4
4
4
4
	
59
4
3
3
3
3
4
4
	
60
4
3
3
3
2
3
4

In [27]:

    

summary(DataOutback)


    

    
Out[27]:





      RRS         TampilanS         MenuS         PackagingS       WifiS      
 Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
 1st Qu.:3.00   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:3.00   1st Qu.:2.000  
 Median :4.00   Median :3.000   Median :3.000   Median :3.50   Median :3.000  
 Mean   :3.55   Mean   :3.283   Mean   :3.117   Mean   :3.35   Mean   :2.733  
 3rd Qu.:4.00   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.00   3rd Qu.:3.000  
 Max.   :5.00   Max.   :4.000   Max.   :5.000   Max.   :5.00   Max.   :5.000  
  PembayaranS      PelayananS   
 Min.   :2.000   Min.   :2.000  
 1st Qu.:4.000   1st Qu.:4.000  
 Median :4.000   Median :4.000  
 Mean   :3.817   Mean   :3.767  
 3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000  

In [28]:

    

cor(DataOutback)


    

    
Out[28]:





RRS
TampilanS
MenuS
PackagingS
WifiS
PembayaranS
PelayananS

	
RRS
1.0000000
0.3481285
0.2399796
0.3352675
0.2503176
0.4586434
0.3464381
	
TampilanS
0.3481285
1.0000000
0.6101810
0.3305556
0.2848860
0.1617495
0.2747542
	
MenuS
0.23997960
0.61018097
1.00000000
0.57954616
0.43302687
0.07707719
0.18330124
	
PackagingS
0.3352675
0.3305556
0.5795462
1.0000000
0.5945836
0.4256719
0.3455232
	
WifiS
0.2503176
0.2848860
0.4330269
0.5945836
1.0000000
0.3000293
0.3205987
	
PembayaranS
0.45864345
0.16174953
0.07707719
0.42567194
0.30002928
1.00000000
0.53201680
	
PelayananS
0.3464381
0.2747542
0.1833012
0.3455232
0.3205987
0.5320168
1.0000000

In [29]:

    

# qplot(DataOutback,
#       x = DataOutback$RRS,
#       y = DataOutback$TampilanS,
#       position = position_jitter(w = 0.1, h = 0.1),
#       xlab = "Retention Rate Suharti",
#       ylab = "Tampilan Interior Suharti",
#       main = "Hubungan Tampilan Interior dengan Retention Rate")


    

In [30]:

    

# qplot(x = RRS,
#       TampilanS,
#       data = DataOutback,
#       geom = c("point", "smooth"),
#       method = "lm",
#       xlab = "Retention Rate Suharti",
#       ylab = "Tampilan Interior Suharti",
#       main = "Hubungan Tampilan Interior dengan Retention Rate",
#       formula = y ~ x)


    

In [31]:

    

regression_RRO_TampilanO = lm(RRS ~ TampilanS, data = DataOutback)
summary(regression_RRO_TampilanO)


    

    
Out[31]:





Call:
lm(formula = RRS ~ TampilanS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4228 -0.4228  0.1283  0.5772  2.0260 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.0763     0.5323   3.900 0.000252 ***
TampilanS     0.4488     0.1587   2.828 0.006417 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8425 on 58 degrees of freedom
Multiple R-squared:  0.1212,	Adjusted R-squared:  0.106 
F-statistic: 7.999 on 1 and 58 DF,  p-value: 0.006417

In [32]:

    

# qplot(x = RRS,
#       y = MenuS,
#       data = DataOutback,
#       geom = c("point"),
#       position = position_jitter(w = 0.1, h = 0.1),
#       method = "lm",
#       xlab = "Retention Rate Suharti",
#       ylab = "Menu SuhartI",
#       main = "Hubungan Tampilan Menu dengan Retention Rate",
#       formula = y ~ x)


    

In [33]:

    

# qplot(x = RRS,
#       y = MenuS,
#       data = DataOutback,
#       geom = c("point", "smooth"),
#       method = "lm",
#       xlab = "Retention Rate Suharti",
#       ylab = "Menu Sharti",
#       main = "Hubungan Tampilan Menu dengan Retention Rate",
#       formula = y ~ x)


    

In [34]:

    

regression_RRO_MenuO = lm(RRS ~ MenuS, data = DataOutback)
summary(regression_RRO_MenuO)


    

    
Out[34]:





Call:
lm(formula = RRS ~ MenuS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5190 -0.5190  0.2152  0.4810  1.7468 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.7215     0.4542   5.991 1.41e-07 ***
MenuS         0.2658     0.1412   1.883   0.0648 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8725 on 58 degrees of freedom
Multiple R-squared:  0.05759,	Adjusted R-squared:  0.04134 
F-statistic: 3.544 on 1 and 58 DF,  p-value: 0.06477

In [35]:

    

# qplot(x = RRS,
#       y = PackagingS,
#       data = DataOutback,
#       geom = c("point"),
#       position = position_jitter(w = 0.1, h = 0.1),
#       xlab = "Retention Rate Suharti",
#       ylab = "Menu Suharti",
#       main = "Hubungan Tampilan Packaging dengan Retention Rate",
#       formula = y ~ x)


    

In [36]:

    

# qplot(x = RRS,
#       y = PackagingS,
#       data = DataOutback,
#       geom = c("point", "smooth"),
#       method = "lm",
#       xlab = "Retention Rate Suharti",
#       ylab = "Packaging Suharti",
#       main = "Hubungan Tampilan Packaging Makanan dengan Retention Rate",
#       formula = y ~ x)


    

In [37]:

    

regression_RRO_MenuO = lm(RRS ~ PackagingS, data = DataOutback)
summary(regression_RRO_MenuO)


    

    
Out[37]:





Call:
lm(formula = RRS ~ PackagingS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4224 -0.7869  0.2131  0.5776  1.5776 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.3291     0.4635   5.025 5.14e-06 ***
PackagingS    0.3644     0.1345   2.710  0.00883 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8467 on 58 degrees of freedom
Multiple R-squared:  0.1124,	Adjusted R-squared:  0.0971 
F-statistic: 7.345 on 1 and 58 DF,  p-value: 0.008829

In [38]:

    

# qplot(x = RRS,
#       y = WifiS,
#       data = DataOutback,
#       geom = c("point"),
#       position = position_jitter(w = 0.1, h = 0.1),
#       xlab = "Retention Rate Suharti",
#       ylab = "Wifi Suharti",
#       main = "Hubungan Wifi dengan Retention Rate pada Suharti",
#       formula = y ~ x)


    

In [39]:

    

# qplot(x = RRS,
#       y = WifiS,
#       data = DataOutback,
#       geom = c("point", "smooth"),
#       method = "lm",
#       xlab = "Retention Rate Suharti",
#       ylab = "Wifi Suharti",
#       main = "Hubungan Wifi dengan Retention Rate",
#       formula = y ~ x)


    

In [40]:

    

regression_RRO_wifiO = lm(RRS ~ WifiS, data = DataOutback)
summary(regression_RRO_wifiO)


    

    
Out[40]:





Call:
lm(formula = RRS ~ WifiS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3507 -0.6225  0.1057  0.6493  1.6493 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.8070     0.3937   7.130 1.77e-09 ***
WifiS         0.2718     0.1380   1.969   0.0537 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8701 on 58 degrees of freedom
Multiple R-squared:  0.06266,	Adjusted R-squared:  0.0465 
F-statistic: 3.877 on 1 and 58 DF,  p-value: 0.05373

In [41]:

    

# qplot(x = RRS,
#       y = PembayaranS,
#       data = DataOutback,
#       geom = c("point"),
#       position = position_jitter(w = 0.1, h = 0.1),
#       xlab = "Retention Rate Suharti",
#       ylab = "Pembayaran Suharti",
#       main = "Hubungan Servis Permbayaran dengan Retention Rate pada Suharti",
#       formula = y ~ x)


    

In [42]:

    

# qplot(x = RRS,
#       y = PembayaranS,
#       data = DataOutback,
#       geom = c("point", "smooth"),
#       method = "lm",
#       xlab = "Retention Rate Suhari",
#       ylab = "Service Pembayaran Suharti",
#       main = "Hubungan Tampilan Menu dengan Retention Rate",
#       formula = y ~ x)


    

In [43]:

    

regression_RRO_PembayaranO = lm(RRS ~ PembayaranS, data = DataOutback)
summary(regression_RRO_PembayaranO)


    

    
Out[43]:





Call:
lm(formula = RRS ~ PembayaranS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0152 -0.6701  0.3300  0.3300  1.9848 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.0508     0.6441   1.631 0.108246    
PembayaranS   0.6548     0.1666   3.931 0.000228 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7987 on 58 degrees of freedom
Multiple R-squared:  0.2104,	Adjusted R-squared:  0.1967 
F-statistic: 15.45 on 1 and 58 DF,  p-value: 0.0002283

In [44]:

    

# qplot(x = RRS,
#       y = PelayananS,
#       data = DataOutback,
#       geom = c("point"),
#       position = position_jitter(w = 0.1, h = 0.1),
#       xlab = "Retention Rate Suharti",
#       ylab = "Pelayanan Suharti",
#       main = "Hubungan Pelayanan dengan Retention Rate pada Suharti",
#       formula = y ~ x)


    

In [45]:

    

regression_RRO_PelayananO = lm(RRS ~ PelayananS, data = DataOutback)
summary(regression_RRO_PelayananO)


    

    
Out[45]:





Call:
lm(formula = RRS ~ PelayananS, data = DataOutback)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1056 -0.6853  0.3147  0.3147  1.8944 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   1.3665     0.7839   1.743   0.0866 . 
PelayananS    0.5797     0.2061   2.813   0.0067 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8431 on 58 degrees of freedom
Multiple R-squared:   0.12,	Adjusted R-squared:  0.1048 
F-statistic: 7.911 on 1 and 58 DF,  p-value: 0.006696

In [46]:

    

reg_all <- lm(RRS ~ TampilanS + MenuS + PackagingS + WifiS + PembayaranS + PelayananS, data = DataOutback)
summary(reg_all)


    

    
Out[46]:





Call:
lm(formula = RRS ~ TampilanS + MenuS + PackagingS + WifiS + PembayaranS + 
    PelayananS, data = DataOutback)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.51546 -0.55038  0.05472  0.45182  2.29339 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.107971   0.852189  -0.127   0.8997  
TampilanS    0.313773   0.193648   1.620   0.1111  
MenuS        0.006011   0.196368   0.031   0.9757  
PackagingS   0.082370   0.190518   0.432   0.6672  
WifiS        0.008822   0.159135   0.055   0.9560  
PembayaranS  0.500384   0.211446   2.366   0.0216 *
PelayananS   0.105973   0.236655   0.448   0.6561  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7888 on 53 degrees of freedom
Multiple R-squared:  0.2961,	Adjusted R-squared:  0.2165 
F-statistic: 3.717 on 6 and 53 DF,  p-value: 0.003707

In [47]:

    

library(leaps)
regsubsets.out <-regsubsets(RRS ~ TampilanS + MenuS + PackagingS + WifiS + PembayaranS + PelayananS,
               data = DataOutback,
               nbest = 1,       # 1 best model for each number of predictors
               nvmax = NULL,    # NULL for no limit on number of variables
               force.in = NULL, force.out = NULL,
               method = "exhaustive")
regsubsets.out


    

    
Out[47]:





Subset selection object
Call: regsubsets.formula(RRS ~ TampilanS + MenuS + PackagingS + WifiS + 
    PembayaranS + PelayananS, data = DataOutback, nbest = 1, 
    nvmax = NULL, force.in = NULL, force.out = NULL, method = "exhaustive")
6 Variables  (and intercept)
            Forced in Forced out
TampilanS       FALSE      FALSE
MenuS           FALSE      FALSE
PackagingS      FALSE      FALSE
WifiS           FALSE      FALSE
PembayaranS     FALSE      FALSE
PelayananS      FALSE      FALSE
1 subsets of each size up to 6
Selection Algorithm: exhaustive

In [48]:

    

summary.out <- summary(regsubsets.out)
as.data.frame(summary.out$outmat)


    

    
Out[48]:





TampilanS
MenuS
PackagingS
WifiS
PembayaranS
PelayananS

	
1  ( 1 )
 
 
 
 
*
 
	
2  ( 1 )
*
 
 
 
*
 
	
3  ( 1 )
*
 
*
 
*
 
	
4  ( 1 )
*
 
*
 
*
*
	
5  ( 1 )
*
 
*
*
*
*
	
6  ( 1 )
*
*
*
*
*
*

In [49]:

    

# plot(regsubsets.out, scale = "adjr2", main = "Adjusted R^2")


    

In [50]:

    

reg_best <- lm(RRS ~ TampilanS + PembayaranS, data = DataOutback)
summary(reg_best)


    

    
Out[50]:





Call:
lm(formula = RRS ~ TampilanS + PembayaranS, data = DataOutback)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.60283 -0.55538  0.08193  0.44462  2.39717 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.1079     0.7247   0.149 0.882191    
TampilanS     0.3627     0.1461   2.483 0.016004 *  
PembayaranS   0.5899     0.1618   3.646 0.000577 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7653 on 57 degrees of freedom
Multiple R-squared:  0.2874,	Adjusted R-squared:  0.2624 
F-statistic:  11.5 on 2 and 57 DF,  p-value: 6.395e-05

In [ ]:

    




    

In [ ]: