Shop Sales Data Analysis Using SPSS: Examining Profit by Category

This assignment demonstrates the analysis of shop sales data using SPSS, focusing on the relationship between product categories and profit totals. The analysis includes the use of GGRAPH to create visualizations such as bar charts and line graphs illustrating mean and sum of profit for each product category. Additionally, a decision tree is constructed to predict product categories based on profit totals, and ONEWAY ANOVA is performed to compare the means of profit totals across different product categories, with post-hoc tests (Tukey HSD) to identify significant differences. The analysis provides descriptive statistics, tests for homogeneity of variances, and multiple comparisons to gain a comprehensive understanding of the factors influencing shop sales and profitability. The document also includes syntax and notes generated by SPSS during the analysis process.

Appendix
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Product_Category MEAN(Profit_Total)
[name="MEAN_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Product_Category=col(source(s), name("Product_Category"), unit.category())
DATA: MEAN_Profit_Total=col(source(s), name("MEAN_Profit_Total"))
GUIDE: axis(dim(1), label("Product Class"))
GUIDE: axis(dim(2), label("Mean Profit Total"))
SCALE: cat(dim(1), include("1", "2", "3", "4", "5", "6", "7", "8", "9"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(Product_Category*MEAN_Profit_Total), shape.interior(shape.square))
END GPL.

GGraph
Notes
Output Created 06-OCT-2018 20:57:41
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Notes

Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Product_Categ
ory MEAN(Profit_Total)
[name="MEAN_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Product_Category=col(source
(s),
name("Product_Category"),
unit.category())
DATA:
MEAN_Profit_Total=col(sourc
e(s),
name("MEAN_Profit_Total"))
GUIDE: axis(dim(1),
label("Product Class"))
GUIDE: axis(dim(2),
label("Mean Profit Total"))
SCALE: cat(dim(1),
include("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
SCALE: linear(dim(2),
include(0))
ELEMENT:

interval(position(Product_Cat
egory*MEAN_Profit_Total),
shape.interior(shape.square))
END GPL.
Resources
Processor Time 00:00:00.17
Elapsed Time 00:00:00.17
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
* Chart Builder.

GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Product_Category MEAN(Profit_Total)
[name="MEAN_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Product_Category=col(source(s), name("Product_Category"), unit.category())
DATA: MEAN_Profit_Total=col(source(s), name("MEAN_Profit_Total"))
GUIDE: axis(dim(1), label("Product Class"))
GUIDE: axis(dim(2), label("Mean Profit Total"))
SCALE: cat(dim(1), include("1", "2", "3", "4", "5", "6", "7", "8", "9"))
SCALE: linear(dim(2), include(0))
ELEMENT: line(position(Product_Category*MEAN_Profit_Total), missing.wings())
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:03:07
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>

N of Rows in Working Data
File 1034

Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Product_Categ
ory MEAN(Profit_Total)
[name="MEAN_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Product_Category=col(source
(s),
name("Product_Category"),
unit.category())
DATA:
MEAN_Profit_Total=col(sourc
e(s),
name("MEAN_Profit_Total"))
GUIDE: axis(dim(1),
label("Product Class"))
GUIDE: axis(dim(2),
label("Mean Profit Total"))
SCALE: cat(dim(1),
include("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
SCALE: linear(dim(2),
include(0))
ELEMENT:

line(position(Product_Categor
y*MEAN_Profit_Total),
missing.wings())
END GPL.
Resources
Processor Time 00:00:00.13
Elapsed Time 00:00:00.13
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Product_Category SUM(Profit_Total)
[name="SUM_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Product_Category=col(source(s), name("Product_Category"), unit.category())
DATA: SUM_Profit_Total=col(source(s), name("SUM_Profit_Total"))
GUIDE: axis(dim(1), label("Product Class"))
GUIDE: axis(dim(2), label("Sum Profit Total"))
SCALE: cat(dim(1), include("1", "2", "3", "4", "5", "6", "7", "8", "9"))
SCALE: linear(dim(2), include(0))
ELEMENT: line(position(Product_Category*SUM_Profit_Total), missing.wings())
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:05:22
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034

Notes
Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Product_Categ
ory SUM(Profit_Total)
[name="SUM_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Product_Category=col(source
(s),
name("Product_Category"),
unit.category())
DATA:
SUM_Profit_Total=col(source
(s),
name("SUM_Profit_Total"))
GUIDE: axis(dim(1),
label("Product Class"))
GUIDE: axis(dim(2),
label("Sum Profit Total"))
SCALE: cat(dim(1),
include("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
SCALE: linear(dim(2),

include(0))
ELEMENT:
line(position(Product_Categor
y*SUM_Profit_Total),
missing.wings())
END GPL.
Resources
Processor Time 00:00:00.17
Elapsed Time 00:00:00.14
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

* Decision Tree.
TREE Product_Category [o] BY Profit_Total [s]
/TREE DISPLAY=TOPDOWN NODES=BOTH BRANCHSTATISTICS=YES NODEDEFS=YES SCALE=AUTO
/DEPCATEGORIES USEVALUES=[1 2 3 4 5 6 7 8 9]
/PRINT MODELSUMMARY CLASSIFICATION TREETABLE
/METHOD TYPE=CHAID
/GROWTHLIMIT MAXDEPTH=AUTO MINPARENTSIZE=100 MINCHILDSIZE=50
/VALIDATION TYPE=NONE OUTPUT=BOTHSAMPLES
/CHAID ALPHASPLIT=0.05 ALPHAMERGE=0.05 SPLITMERGED=NO CONVERGE=0.001
MAXITERATIONS=100 ADJUST=BONFERRONI INTERVALS=10
/COSTS EQUAL
/SCORES EQUALINCREMENTS.
Classification Tree
Notes
Output Created 06-OCT-2018 21:13:26
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>

N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing
Handling of user-defined
missing values of nominal
independent variables
depends on the growing
method.
Cases Used
Only cases with valid data for
the dependent variable and
some or all independent
variables are used in
computing any statistics.
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
Warnings
One or more values specified on the DEPCATEGORIES
subcommand USEVALUES keyword do not exist in the training
sample.
Gain summary Tables are not displayed because profits are
undefined.
Target category gains tables are not displayed because target
categories are undefined.
Model Summary
Specifications Growing Method CHAID
Dependent Variable Product Class

Independent Variables Profit Total
Validation None
Maximum Tree Depth 3
Minimum Cases in Parent
Node 100
Minimum Cases in Child
Node 50
Results
Independent Variables
Included Profit Total
Number of Nodes 5
Number of Terminal Nodes 4
Depth 1

Tree Table
Node Mens shoes Mens clothing Womens shoes womens clothing customis
e
N Percent N Percent N Percent N Percent N
0 91 14.9% 78 12.7% 13 2.1% 348 56.9% 27

1 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0
2 0 0.0% 0 0.0% 0 0.0% 348 100.0% 0
3 0 0.0% 78 85.7% 13 14.3% 0 0.0% 0
4 91 75.8% 0 0.0% 0 0.0% 0 0.0% 27
Tree Table
Node customise boys shoes girls shoes girls clothing Total
Percent N Percent N Percent N Percent N Percent
0 4.4% 51 8.3% 2 0.3% 2 0.3% 612 100.0%
1 0.0% 51 96.2% 0 0.0% 2 3.8% 53 8.7%
2 0.0% 0 0.0% 0 0.0% 0 0.0% 348 56.9%
3 0.0% 0 0.0% 0 0.0% 0 0.0% 91 14.9%
4 22.5% 0 0.0% 2 1.7% 0 0.0% 120 19.6%
Tree Table
Node Predicted
Category
Parent Node Primary Independent Variable
Variable Sig. Chi-Square df Split Values
0 womens clothing
1 boys shoes 0 Profit Total .000 664.001 3 <= 4.000
2 womens clothing 0 Profit Total .000 664.001 3 (4.000, 4.200]
3 Mens clothing 0 Profit Total .000 664.001 3 (4.200, 6.500]
4 Mens shoes 0 Profit Total .000 664.001 3 > 6.500

Growing Method: CHAID
Dependent Variable: Product Class
a. Bonferroni adjusted
Classification
Observed Predicted
Mens shoes Mens clothing Womens shoes womens clothing customise
Mens shoes 91 0 0 0 0
Mens clothing 0 78 0 0 0
Womens shoes 0 13 0 0 0
womens clothing 0 0 0 348 0
customise 27 0 0 0 0
boys shoes 0 0 0 0 0
girls shoes 2 0 0 0 0
girls clothing 0 0 0 0 0
Overall Percentage 19.6% 14.9% 0.0% 56.9% 0.0%
Classification
Observed Predicted
boys shoes girls shoes girls clothing Percent Correct
Mens shoes 0 0 0 100.0%
Mens clothing 0 0 0 100.0%
Womens shoes 0 0 0 0.0%
womens clothing 0 0 0 100.0%

customise 0 0 0 0.0%
boys shoes 51 0 0 100.0%
girls shoes 0 0 0 0.0%
girls clothing 2 0 0 0.0%
Overall Percentage 8.7% 0.0% 0.0% 92.8%
Growing Method: CHAID
Dependent Variable: Product Class
ONEWAY Profit_Total BY Product_Category
/STATISTICS DESCRIPTIVES HOMOGENEITY
/MISSING ANALYSIS
/POSTHOC=TUKEY ALPHA(0.05).
Oneway
Notes
Output Created 06-OCT-2018 21:14:26
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1

Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.
Syntax
ONEWAY Profit_Total BY
Product_Category
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS
/POSTHOC=TUKEY
ALPHA(0.05).
Resources
Processor Time 00:00:00.03
Elapsed Time 00:00:00.03
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
Descriptives
Profit Total
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean

Lower Bound Upper Bound
Mens shoes 91 15.8934 .40738 .04270 15.8086 15.9782
Mens clothing 78 6.0000 .00000 .00000 6.0000 6.0000
Womens shoes 13 6.5000 .00000 .00000 6.5000 6.5000
womens clothing 348 4.2000 .00000 .00000 4.2000 4.2000
customise 27 25.0000 .00000 .00000 25.0000 25.0000
boys shoes 51 3.3000 .00000 .00000 3.3000 3.3000
girls shoes 2 7.0000 .00000 .00000 7.0000 7.0000
girls clothing 2 4.0000 .00000 .00000 4.0000 4.0000
Total 612 7.0681 5.64691 .22826 6.6199 7.5164
Descriptives
Profit Total
Minimum Maximum
Mens shoes 15.80 18.00
Mens clothing 6.00 6.00
Womens shoes 6.50 6.50
womens clothing 4.20 4.20

customise 25.00 25.00
boys shoes 3.30 3.30
girls shoes 7.00 7.00
girls clothing 4.00 4.00
Total 3.30 25.00
Test of Homogeneity of Variances
Profit Total
Levene Statistic df1 df2 Sig.
16.253 7 604 .000
ANOVA
Profit Total
Sum of Squares df Mean Square F Sig.
Between Groups 19468.353 7 2781.193 112468.919 .000
Within Groups 14.936 604 .025
Total 19483.289 611
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD
(I) Product Class (J) Product Class Mean Difference
(I-J)
Std. Error Sig. 95%
Confidence
Interval

Lower Bound
Mens shoes
Mens clothing 9.89341* .02426 .000 9.8196
Womens shoes 9.39341* .04663 .000 9.2516
womens clothing 11.69341* .01851 .000 11.6371
customise -9.10659* .03446 .000 -9.2114
boys shoes 12.59341* .02751 .000 12.5097
girls shoes 8.89341* .11241 .000 8.5515
girls clothing 11.89341* .11241 .000 11.5515
Mens clothing
Mens shoes -9.89341* .02426 .000 -9.9672
Womens shoes -.50000* .04711 .000 -.6433
womens clothing 1.80000* .01970 .000 1.7401
customise -19.00000* .03511 .000 -19.1068
boys shoes 2.70000* .02832 .000 2.6139
girls shoes -1.00000* .11261 .000 -1.3425
girls clothing 2.00000* .11261 .000 1.6575
Womens shoes
Mens shoes -9.39341* .04663 .000 -9.5352
Mens clothing .50000* .04711 .000 .3567
womens clothing 2.30000* .04442 .000 2.1649
customise -18.50000* .05309 .000 -18.6615
boys shoes 3.20000* .04886 .000 3.0514
girls shoes -.50000* .11944 .001 -.8633
girls clothing 2.50000* .11944 .000 2.1367
womens clothing Mens shoes -11.69341* .01851 .000 -11.7497
Mens clothing -1.80000* .01970 .000 -1.8599

Womens shoes -2.30000* .04442 .000 -2.4351
customise -20.80000* .03142 .000 -20.8956
boys shoes .90000* .02358 .000 .8283
girls shoes -2.80000* .11151 .000 -3.1392
girls clothing .20000 .11151 .625 -.1392
customise
Mens shoes 9.10659* .03446 .000 9.0018
Mens clothing 19.00000* .03511 .000 18.8932
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD
(I) Product Class (J) Product Class 95% Confidence Interval
Upper Bound
Mens shoes
Mens clothing 9.9672*
Womens shoes 9.5352*
womens clothing 11.7497*
customise -9.0018*
boys shoes 12.6771*
girls shoes 9.2353*
girls clothing 12.2353*
Mens clothing Mens shoes -9.8196*
Womens shoes -.3567*
womens clothing 1.8599*
customise -18.8932*

boys shoes 2.7861*
girls shoes -.6575*
girls clothing 2.3425*
Womens shoes
Mens shoes -9.2516*
Mens clothing .6433*
womens clothing 2.4351*
customise -18.3385*
boys shoes 3.3486*
girls shoes -.1367*
girls clothing 2.8633*
womens clothing
Mens shoes -11.6371*
Mens clothing -1.7401*
Womens shoes -2.1649*
customise -20.7044*
boys shoes .9717*
girls shoes -2.4608*
girls clothing .5392
customise
Mens shoes 9.2114*
Mens clothing 19.1068*
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD

(I) Product Class (J) Product Class Mean Difference
(I-J)
Std. Error Sig. 95%
Confidence
Interval
Lower Bound
customise Womens shoes 18.50000* .05309 .000 18.3385
womens clothing 20.80000* .03142 .000 20.7044
boys shoes 21.70000* .03743 .000 21.5862
girls shoes 18.00000* .11524 .000 17.6495
girls clothing 21.00000* .11524 .000 20.6495
boys shoes
Mens shoes -12.59341* .02751 .000 -12.6771
Mens clothing -2.70000* .02832 .000 -2.7861
Womens shoes -3.20000* .04886 .000 -3.3486
womens clothing -.90000* .02358 .000 -.9717
customise -21.70000* .03743 .000 -21.8138
girls shoes -3.70000* .11335 .000 -4.0448
girls clothing -.70000* .11335 .000 -1.0448
girls shoes
Mens shoes -8.89341* .11241 .000 -9.2353
Mens clothing 1.00000* .11261 .000 .6575
Womens shoes .50000* .11944 .001 .1367
womens clothing 2.80000* .11151 .000 2.4608
customise -18.00000* .11524 .000 -18.3505
boys shoes 3.70000* .11335 .000 3.3552
girls clothing 3.00000* .15725 .000 2.5217
girls clothing Mens shoes -11.89341* .11241 .000 -12.2353
Mens clothing -2.00000* .11261 .000 -2.3425

Womens shoes -2.50000* .11944 .000 -2.8633
womens clothing -.20000* .11151 .625 -.5392
customise -21.00000* .11524 .000 -21.3505
boys shoes .70000* .11335 .000 .3552
girls shoes -3.00000* .15725 .000 -3.4783
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD
(I) Product Class (J) Product Class 95% Confidence Interval
Upper Bound
customise Womens shoes 18.6615*
womens clothing 20.8956*
boys shoes 21.8138*
girls shoes 18.3505*
girls clothing 21.3505*
boys shoes
Mens shoes -12.5097*
Mens clothing -2.6139*
Womens shoes -3.0514*
womens clothing -.8283*
customise -21.5862*
girls shoes -3.3552*
girls clothing -.3552*
girls shoes Mens shoes -8.5515*

Mens clothing 1.3425*
Womens shoes .8633*
womens clothing 3.1392*
customise -17.6495*
boys shoes 4.0448*
girls clothing 3.4783*
girls clothing
Mens shoes -11.5515*
Mens clothing -1.6575*
Womens shoes -2.1367*
womens clothing .1392*
customise -20.6495*
boys shoes 1.0448*
girls shoes -2.5217*
*. The mean difference is significant at the 0.05 level.
Homogeneous Subsets
Profit Total
Tukey HSD
Product Class N Subset for alpha = 0.05
1 2 3 4 5 6

boys shoes 51 3.3000
girls clothing 2 4.0000
womens clothing 348 4.2000
Mens clothing 78 6.0000
Womens shoes 13 6.5000
girls shoes 2 7.0000
Mens shoes 91 15.8934
customise 27
Sig. 1.000 .262 1.000 1.000 1.000 1.000
Profit Total
Tukey HSD
Product Class Subset for alpha = 0.05
7
boys shoes
girls clothing
womens clothing
Mens clothing
Womens shoes
girls shoes
Mens shoes
customise 25.0000
Sig. 1.000

Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 6.895.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not
guaranteed.
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Product_Category SUM(COGS)
[name="SUM_COGS"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Product_Category=col(source(s), name("Product_Category"), unit.category())
DATA: SUM_COGS=col(source(s), name("SUM_COGS"))
GUIDE: axis(dim(1), label("Product Class"))
GUIDE: axis(dim(2), label("Sum Cost of Goods ($)"))
SCALE: cat(dim(1), include("1", "2", "3", "4", "5", "6", "7", "8", "9"))
SCALE: linear(dim(2), include(0))
ELEMENT: line(position(Product_Category*SUM_COGS), missing.wings())
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:15:51
Comments

Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Notes

Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Product_Categ
ory SUM(COGS)
[name="SUM_COGS"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Product_Category=col(source
(s),
name("Product_Category"),
unit.category())
DATA:
SUM_COGS=col(source(s),
name("SUM_COGS"))
GUIDE: axis(dim(1),
label("Product Class"))
GUIDE: axis(dim(2),
label("Sum Cost of Goods
($)"))
SCALE: cat(dim(1),
include("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
SCALE: linear(dim(2),
include(0))
ELEMENT:

line(position(Product_Categor
y*SUM_COGS),
missing.wings())
END GPL.
Resources
Processor Time 00:00:00.14
Elapsed Time 00:00:00.14
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Product_Category MEAN(COGS)
[name="MEAN_COGS"] MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Product_Category=col(source(s), name("Product_Category"), unit.category())
DATA: MEAN_COGS=col(source(s), name("MEAN_COGS"))
GUIDE: axis(dim(1), label("Product Class"))
GUIDE: axis(dim(2), label("Mean Cost of Goods ($)"))
SCALE: cat(dim(1), include("1", "2", "3", "4", "5", "6", "7", "8", "9"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(Product_Category*MEAN_COGS), shape.interior(shape.square))
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:16:37
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Notes

Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Product_Categ
ory MEAN(COGS)
[name="MEAN_COGS"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Product_Category=col(source
(s),
name("Product_Category"),
unit.category())
DATA:
MEAN_COGS=col(source(s),
name("MEAN_COGS"))
GUIDE: axis(dim(1),
label("Product Class"))
GUIDE: axis(dim(2),
label("Mean Cost of Goods
($)"))
SCALE: cat(dim(1),
include("1", "2", "3", "4", "5",
"6", "7", "8", "9"))
SCALE: linear(dim(2),
include(0))
ELEMENT:

interval(position(Product_Cat
egory*MEAN_COGS),
shape.interior(shape.square))
END GPL.
Resources
Processor Time 00:00:00.17
Elapsed Time 00:00:00.14
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
* Decision Tree.
TREE Product_Category [o] BY COGS [s]
/TREE DISPLAY=TOPDOWN NODES=BOTH BRANCHSTATISTICS=YES NODEDEFS=YES SCALE=AUTO

/DEPCATEGORIES USEVALUES=[1 2 3 4 5 6 7 8 9]
/PRINT MODELSUMMARY CLASSIFICATION TREETABLE
/METHOD TYPE=CHAID
/GROWTHLIMIT MAXDEPTH=AUTO MINPARENTSIZE=100 MINCHILDSIZE=50
/VALIDATION TYPE=NONE OUTPUT=BOTHSAMPLES
/CHAID ALPHASPLIT=0.05 ALPHAMERGE=0.05 SPLITMERGED=NO CONVERGE=0.001
MAXITERATIONS=100 ADJUST=BONFERRONI INTERVALS=10
/COSTS EQUAL
/SCORES EQUALINCREMENTS.
Classification Tree
Notes
Output Created 06-OCT-2018 21:18:15
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034

Missing Value Handling
Definition of Missing
Handling of user-defined
missing values of nominal
independent variables
depends on the growing
method.
Cases Used
Only cases with valid data for
the dependent variable and
some or all independent
variables are used in
computing any statistics.
Notes

Syntax
TREE Product_Category [o] BY COGS
[s]
/TREE DISPLAY=TOPDOWN
NODES=BOTH
BRANCHSTATISTICS=YES
NODEDEFS=YES SCALE=AUTO
/DEPCATEGORIES USEVALUES=[1 2
3 4 5 6 7 8 9]
/PRINT MODELSUMMARY
CLASSIFICATION TREETABLE
/METHOD TYPE=CHAID
/GROWTHLIMIT MAXDEPTH=AUTO
MINPARENTSIZE=100
MINCHILDSIZE=50
/VALIDATION TYPE=NONE
OUTPUT=BOTHSAMPLES
/CHAID ALPHASPLIT=0.05
ALPHAMERGE=0.05
SPLITMERGED=NO CONVERGE=0.001
MAXITERATIONS=100
ADJUST=BONFERRONI
INTERVALS=10
/COSTS EQUAL
/SCORES EQUALINCREMENTS.
Resources
Processor Time 00:00:00.09
Elapsed Time 00:00:00.08
Files Saved Rules File
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

Warnings
One or more values specified on the DEPCATEGORIES
subcommand USEVALUES keyword do not exist in the training
sample.
Gain summary Tables are not displayed because profits are
undefined.
Target category gains tables are not displayed because target
categories are undefined.
Model Summary
Specifications
Growing Method CHAID
Dependent Variable Product Class
Independent Variables Cost of Goods ($)
Validation None
Maximum Tree Depth 3
Minimum Cases in Parent
Node 100
Minimum Cases in Child
Node 50
Results
Independent Variables
Included Cost of Goods ($)
Number of Nodes 5
Number of Terminal Nodes 4
Depth 1

Tree Table
Node Mens shoes Mens clothing Womens shoes womens clothing customis
e
N Percent N Percent N Percent N Percent N
0 91 14.9% 78 12.7% 13 2.1% 348 56.9% 27

1 0 0.0% 78 100.0% 0 0.0% 0 0.0% 0
2 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0
3 0 0.0% 0 0.0% 0 0.0% 348 100.0% 0
4 91 67.4% 0 0.0% 13 9.6% 0 0.0% 27
Tree Table
Node customise boys shoes girls shoes girls clothing Total
Percent N Percent N Percent N Percent N Percent
0 4.4% 51 8.3% 2 0.3% 2 0.3% 612 100.0%
1 0.0% 0 0.0% 0 0.0% 0 0.0% 78 12.7%
2 0.0% 51 100.0% 0 0.0% 0 0.0% 51 8.3%
3 0.0% 0 0.0% 0 0.0% 0 0.0% 348 56.9%
4 20.0% 0 0.0% 2 1.5% 2 1.5% 135 22.1%
Tree Table
Node Predicted Category Parent Node Primary Independent Variable
Variable Sig. Chi-Square df
0 womens clothing
1 Mens clothing 0 Cost of Goods ($) .000 595.926 3
2 boys shoes 0 Cost of Goods ($) .000 595.926 3
3 womens clothing 0 Cost of Goods ($) .000 595.926 3
4 Mens shoes 0 Cost of Goods ($) .000 595.926 3
Tree Table

Node Primary Independent Variable
Split Values
0
1 <= 1.000
2 (1.000, 2.550]
3 (2.550, 2.700]
4 > 2.700
Growing Method: CHAID
Dependent Variable: Product Class
a. Bonferroni adjusted
Classification
Observed Predicted
Mens shoes Mens clothing Womens shoes womens clothing customise
Mens shoes 91 0 0 0 0
Mens clothing 0 78 0 0 0

Womens shoes 13 0 0 0 0
womens clothing 0 0 0 348 0
customise 27 0 0 0 0
boys shoes 0 0 0 0 0
girls shoes 2 0 0 0 0
girls clothing 2 0 0 0 0
Overall Percentage 22.1% 12.7% 0.0% 56.9% 0.0%
Classification
Observed Predicted
boys shoes girls shoes girls clothing Percent Correct
Mens shoes 0 0 0 100.0%
Mens clothing 0 0 0 100.0%
Womens shoes 0 0 0 0.0%
womens clothing 0 0 0 100.0%
customise 0 0 0 0.0%
boys shoes 51 0 0 100.0%
girls shoes 0 0 0 0.0%
girls clothing 0 0 0 0.0%
Overall Percentage 8.3% 0.0% 0.0% 92.8%
Growing Method: CHAID
Dependent Variable: Product Class
ONEWAY COGS BY Product_Category

/STATISTICS DESCRIPTIVES HOMOGENEITY
/MISSING ANALYSIS
/POSTHOC=TUKEY ALPHA(0.05).
Oneway
Notes
Output Created 06-OCT-2018 21:18:32
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.

Syntax
ONEWAY COGS BY
Product_Category
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS
/POSTHOC=TUKEY
ALPHA(0.05).
Resources
Processor Time 00:00:00.03
Elapsed Time 00:00:00.03
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
Descriptives
Cost of Goods ($)

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean
Lower Bound Upper Bound
Mens shoes 91 3.5000 .00000 .00000 3.5000 3.5000
Mens clothing 78 1.0000 .00000 .00000 1.0000 1.0000
Womens shoes 13 5.2000 .00000 .00000 5.2000 5.2000
womens clothing 348 2.7000 .00000 .00000 2.7000 2.7000
customise 27 9.8000 .00000 .00000 9.8000 9.8000
boys shoes 51 2.5500 .00000 .00000 2.5500 2.5500
girls shoes 2 8.0000 .00000 .00000 8.0000 8.0000
girls clothing 2 3.2500 .35355 .25000 .0734 6.4266
Total 612 2.9752 1.68641 .06817 2.8414 3.1091
Descriptives
Cost of Goods ($)
Minimum Maximum
Mens shoes 3.50 3.50
Mens clothing 1.00 1.00
Womens shoes 5.20 5.20
womens clothing 2.70 2.70
customise 9.80 9.80
boys shoes 2.55 2.55
girls shoes 8.00 8.00
girls clothing 3.00 3.50

Total 1.00 9.80
Test of Homogeneity of Variances
Cost of Goods ($)
Levene Statistic df1 df2 Sig.
. 7 . .
ANOVA
Cost of Goods ($)
Sum of Squares df Mean Square F Sig.
Between Groups 1737.547 7 248.221 1199404.192 .000
Within Groups .125 604 .000
Total 1737.672 611
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Cost of Goods ($)
Tukey HSD
(I) Product Class (J) Product Class Mean Difference
(I-J)
Std. Error Sig. 95%
Confidence
Interval
Lower Bound
Mens shoes Mens clothing 2.50000* .00222 .000 2.4932
Womens shoes -1.70000* .00427 .000 -1.7130
womens clothing .80000* .00169 .000 .7948

customise -6.30000* .00315 .000 -6.3096
boys shoes .95000* .00252 .000 .9423
girls shoes -4.50000* .01028 .000 -4.5313
girls clothing .25000* .01028 .000 .2187
Mens clothing
Mens shoes -2.50000* .00222 .000 -2.5068
Womens shoes -4.20000* .00431 .000 -4.2131
womens clothing -1.70000* .00180 .000 -1.7055
customise -8.80000* .00321 .000 -8.8098
boys shoes -1.55000* .00259 .000 -1.5579
girls shoes -7.00000* .01030 .000 -7.0313
girls clothing -2.25000* .01030 .000 -2.2813
Womens shoes
Mens shoes 1.70000* .00427 .000 1.6870
Mens clothing 4.20000* .00431 .000 4.1869
womens clothing 2.50000* .00406 .000 2.4876
customise -4.60000* .00486 .000 -4.6148
boys shoes 2.65000* .00447 .000 2.6364
girls shoes -2.80000* .01093 .000 -2.8332
girls clothing 1.95000* .01093 .000 1.9168
womens clothing Mens shoes -.80000* .00169 .000 -.8052
Mens clothing 1.70000* .00180 .000 1.6945
Womens shoes -2.50000* .00406 .000 -2.5124
customise -7.10000* .00287 .000 -7.1087
boys shoes .15000* .00216 .000 .1434
girls shoes -5.30000* .01020 .000 -5.3310

girls clothing -.55000* .01020 .000 -.5810
customise
Mens shoes 6.30000* .00315 .000 6.2904
Mens clothing 8.80000* .00321 .000 8.7902
Multiple Comparisons
Dependent Variable: Cost of Goods ($)
Tukey HSD
(I) Product Class (J) Product Class 95% Confidence Interval
Upper Bound
Mens shoes
Mens clothing 2.5068*
Womens shoes -1.6870*
womens clothing .8052*
customise -6.2904*
boys shoes .9577*
girls shoes -4.4687*
girls clothing .2813*
Mens clothing
Mens shoes -2.4932*
Womens shoes -4.1869*
womens clothing -1.6945*
customise -8.7902*
boys shoes -1.5421*
girls shoes -6.9687*
girls clothing -2.2187*
Womens shoes Mens shoes 1.7130*

Mens clothing 4.2131*
womens clothing 2.5124*
customise -4.5852*
boys shoes 2.6636*
girls shoes -2.7668*
girls clothing 1.9832*
womens clothing
Mens shoes -.7948*
Mens clothing 1.7055*
Womens shoes -2.4876*
customise -7.0913*
boys shoes .1566*
girls shoes -5.2690*
girls clothing -.5190*
customise
Mens shoes 6.3096*
Mens clothing 8.8098*
Multiple Comparisons
Dependent Variable: Cost of Goods ($)
Tukey HSD
(I) Product Class (J) Product Class Mean Difference
(I-J)
Std. Error Sig. 95%
Confidence
Interval
Lower Bound
customise Womens shoes 4.60000* .00486 .000 4.5852
womens clothing 7.10000* .00287 .000 7.0913

boys shoes 7.25000* .00342 .000 7.2396
girls shoes 1.80000* .01054 .000 1.7679
girls clothing 6.55000* .01054 .000 6.5179
boys shoes
Mens shoes -.95000* .00252 .000 -.9577
Mens clothing 1.55000* .00259 .000 1.5421
Womens shoes -2.65000* .00447 .000 -2.6636
womens clothing -.15000* .00216 .000 -.1566
customise -7.25000* .00342 .000 -7.2604
girls shoes -5.45000* .01037 .000 -5.4815
girls clothing -.70000* .01037 .000 -.7315
girls shoes
Mens shoes 4.50000* .01028 .000 4.4687
Mens clothing 7.00000* .01030 .000 6.9687
Womens shoes 2.80000* .01093 .000 2.7668
womens clothing 5.30000* .01020 .000 5.2690
customise -1.80000* .01054 .000 -1.8321
boys shoes 5.45000* .01037 .000 5.4185
girls clothing 4.75000* .01439 .000 4.7062
girls clothing
Mens shoes -.25000* .01028 .000 -.2813
Mens clothing 2.25000* .01030 .000 2.2187
Womens shoes -1.95000* .01093 .000 -1.9832
womens clothing .55000* .01020 .000 .5190
customise -6.55000* .01054 .000 -6.5821
boys shoes .70000* .01037 .000 .6685
girls shoes -4.75000* .01439 .000 -4.7938

Multiple Comparisons
Dependent Variable: Cost of Goods ($)
Tukey HSD
(I) Product Class (J) Product Class 95% Confidence Interval
Upper Bound
customise Womens shoes 4.6148*
womens clothing 7.1087*
boys shoes 7.2604*
girls shoes 1.8321*
girls clothing 6.5821*
boys shoes
Mens shoes -.9423*
Mens clothing 1.5579*
Womens shoes -2.6364*
womens clothing -.1434*
customise -7.2396*
girls shoes -5.4185*
girls clothing -.6685*
girls shoes
Mens shoes 4.5313*
Mens clothing 7.0313*
Womens shoes 2.8332*
womens clothing 5.3310*
customise -1.7679*
boys shoes 5.4815*
girls clothing 4.7938*

girls clothing
Mens shoes -.2187*
Mens clothing 2.2813*
Womens shoes -1.9168*
womens clothing .5810*
customise -6.5179*
boys shoes .7315*
girls shoes -4.7062*
*. The mean difference is significant at the 0.05 level.
Homogeneous Subsets
Cost of Goods ($)
Tukey HSD
Product Class N Subset for alpha = 0.05
1 2 3 4 5 6
Mens clothing 78 1.0000
boys shoes 51 2.5500
womens clothing 348 2.7000
girls clothing 2 3.2500
Mens shoes 91 3.5000
Womens shoes 13 5.2000
girls shoes 2
customise 27
Sig. 1.000 1.000 1.000 1.000 1.000 1.000

Cost of Goods ($)
Tukey HSD
Product Class Subset for alpha = 0.05
7 8
Mens clothing
boys shoes
womens clothing
girls clothing
Mens shoes
Womens shoes
girls shoes 8.0000
customise 9.8000
Sig. 1.000 1.000
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 6.895.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not
guaranteed.
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Cash_Total MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL

SOURCE: s=userSource(id("graphdataset"))
DATA: Cash_Total=col(source(s), name("Cash_Total"))
DATA: id=col(source(s), name("$CASENUM"), unit.category())
COORD: rect(dim(1), transpose())
GUIDE: axis(dim(1), label("Total purchases paid with Paypal"))
ELEMENT: schema(position(bin.quantile.letter(Cash_Total)), label(id))
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:22:28
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>

N of Rows in Working Data
File 1034
Syntax
GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Cash_Total
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Cash_Total=col(source(s),
name("Cash_Total"))
DATA: id=col(source(s),
name("$CASENUM"),
unit.category())
COORD: rect(dim(1),
transpose())
GUIDE: axis(dim(1),
label("Total purchases paid
with Paypal"))
ELEMENT:
schema(position(bin.quantile.l
etter(Cash_Total)), label(id))
END GPL.
Resources
Processor Time 00:00:00.14
Elapsed Time 00:00:00.14

[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Credit_card_Total MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))

DATA: Credit_card_Total=col(source(s), name("Credit_card_Total"))
DATA: id=col(source(s), name("$CASENUM"), unit.category())
COORD: rect(dim(1), transpose())
GUIDE: axis(dim(1), label("Total purchases paid with Credit card"))
ELEMENT: schema(position(bin.quantile.letter(Credit_card_Total)), label(id))
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:23:17
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>

N of Rows in Working Data
File 1034
Syntax
GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Credit_card_To
tal MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Credit_card_Total=col(source
(s),
name("Credit_card_Total"))
DATA: id=col(source(s),
name("$CASENUM"),
unit.category())
COORD: rect(dim(1),
transpose())
GUIDE: axis(dim(1),
label("Total purchases paid
with Credit card"))
ELEMENT:
schema(position(bin.quantile.l
etter(Credit_card_Total)),
label(id))
END GPL.
Resources Processor Time 00:00:00.13

Elapsed Time 00:00:00.13
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
T-TEST PAIRS=Cash_Total WITH Credit_card_Total (PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.

T-Test
Notes
Output Created 06-OCT-2018 21:25:00
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing User defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on the cases with
no missing or out-of-range
data for any variable in the
analysis.

Syntax
T-TEST PAIRS=Cash_Total
WITH Credit_card_Total
(PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.00
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1
Total purchases paid with
Paypal 2.5417 612 .77310 .03125
Total purchases paid with
Credit card 4.5126 612 6.22208 .25151
Paired Samples Correlations
N Correlation Sig.

Pair 1
Total purchases paid with
Paypal & Total purchases
paid with Credit card
612 -.800 .000
Paired Samples Test
Paired Differences
Mean Std. Deviation Std. Error Mean 95% Confidence
Interval of the
Difference
Lower
Pair 1
Total purchases paid with
Paypal - Total purchases
paid with Credit card
-1.97092 6.85605 .27714 -2.51518
Paired Samples Test
Paired Differences t df Sig. (2-tailed)
95% Confidence
Interval of the
Difference
Upper
Pair 1
Total purchases paid with Paypal
- Total purchases paid with Credit
card
-1.42665 -7.112 611 .000
* Chart Builder.
GGRAPH

/GRAPHDATASET NAME="graphdataset" VARIABLES=Location MEAN(Profit_Total)
[name="MEAN_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Location=col(source(s), name("Location"), unit.category())
DATA: MEAN_Profit_Total=col(source(s), name("MEAN_Profit_Total"))
GUIDE: axis(dim(1), label("Location in shop"))
GUIDE: axis(dim(2), label("Mean Profit Total"))
SCALE: cat(dim(1), include("1.00", "2.00", "3.00", "4.00"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(Location*MEAN_Profit_Total), shape.interior(shape.square))
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 21:29:26
Comments

Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Notes

Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Location
MEAN(Profit_Total)
[name="MEAN_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
Location=col(source(s),
name("Location"),
unit.category())
DATA:
MEAN_Profit_Total=col(sourc
e(s),
name("MEAN_Profit_Total"))
GUIDE: axis(dim(1),
label("Location in shop"))
GUIDE: axis(dim(2),
label("Mean Profit Total"))
SCALE: cat(dim(1),
include("1.00", "2.00", "3.00",
"4.00"))
SCALE: linear(dim(2),
include(0))
ELEMENT:
interval(position(Location*ME

AN_Profit_Total),
shape.interior(shape.square))
END GPL.
Resources
Processor Time 00:00:00.20
Elapsed Time 00:00:00.17
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
ONEWAY Profit_Total BY Location
/STATISTICS DESCRIPTIVES HOMOGENEITY

/MISSING ANALYSIS
/POSTHOC=TUKEY ALPHA(0.05).
Oneway
Notes
Output Created 06-OCT-2018 21:30:12
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Missing Value Handling Definition of Missing User-defined missing values
are treated as missing.

Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.
Syntax
ONEWAY Profit_Total BY
Location
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS
/POSTHOC=TUKEY
ALPHA(0.05).
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav
Descriptives
Profit Total
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum
Lower Bound Upper Bound
front 169 11.3272 4.95568 .38121 10.5746 12.0798 6.00
middle 361 4.2828 .42913 .02259 4.2384 4.3272 4.20

back 55 3.4600 .70648 .09526 3.2690 3.6510 3.30
online 27 25.0000 .00000 .00000 25.0000 25.0000 25.00
Total 612 7.0681 5.64691 .22826 6.6199 7.5164 3.30
Descriptives
Profit Total
Maximum
front 18.00
middle 6.50
back 7.00
online 25.00
Total 25.00
Test of Homogeneity of Variances
Profit Total
Levene Statistic df1 df2 Sig.
4725.999 3 608 .000
ANOVA
Profit Total
Sum of Squares df Mean Square F Sig.

Between Groups 15264.168 3 5088.056 733.219 .000
Within Groups 4219.120 608 6.939
Total 19483.289 611
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD
(I) Location in shop (J) Location in shop Mean Difference
(I-J)
Std. Error Sig. 95%
Confidence
Interval
Lower Bound
front
middle 7.04439* .24553 .000 6.4119
back 7.86722* .40894 .000 6.8137
online -13.67278* .54596 .000 -15.0793
middle
front -7.04439* .24553 .000 -7.6769
back .82283 .38130 .136 -.1595
online -20.71717* .52558 .000 -22.0711
back front -7.86722* .40894 .000 -8.9207

middle -.82283 .38130 .136 -1.8051
online -21.54000* .61902 .000 -23.1347
online
front 13.67278* .54596 .000 12.2663
middle 20.71717* .52558 .000 19.3632
back 21.54000* .61902 .000 19.9453
Multiple Comparisons
Dependent Variable: Profit Total
Tukey HSD
(I) Location in shop (J) Location in shop 95% Confidence Interval
Upper Bound
front
middle 7.6769*
back 8.9207*
online -12.2663*
middle
front -6.4119*
back 1.8051
online -19.3632*
back
front -6.8137*
middle .1595
online -19.9453*
online
front 15.0793*
middle 22.0711*
back 23.1347*

*. The mean difference is significant at the 0.05 level.
Homogeneous Subsets
Profit Total
Tukey HSD
Location in shop N Subset for alpha = 0.05
1 2 3
back 55 3.4600
middle 361 4.2828
front 169 11.3272
online 27 25.0000
Sig. .300 1.000 1.000
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 62.592.
b. The group sizes are unequal. The harmonic mean of the group sizes
is used. Type I error levels are not guaranteed.
SPLIT FILE OFF.

EXAMINE VARIABLES=Knowledge Satisfaction Preference Purchase_intentions Loyalty Recommend
CustomerAttitude BY User_Group
/PLOT NONE
/STATISTICS DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Explore
Notes
Output Created 06-OCT-2018 22:03:38
Comments
Input
Data
C:\Users\me\Desktop\
assignment\
2712879_1621455809_Custo
mer_attributes(1).sav
Active Dataset DataSet1
Filter <none>
Weight <none>

Split File <none>
N of Rows in Working Data
File 602
Missing Value Handling
Definition of Missing
User-defined missing values
for dependent variables are
treated as missing.
Cases Used
Statistics are based on cases
with no missing values for
any dependent variable or
factor used.
Syntax
EXAMINE
VARIABLES=Knowledge
Satisfaction Preference
Purchase_intentions Loyalty
Recommend
CustomerAttitude BY
User_Group
/PLOT NONE
/STATISTICS
DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
[DataSet1] C:\Users\me\Desktop\assignment\2712879_1621455809_Customer_attributes(1).sav

How often they visit the store
Case Processing Summary
How often they visit the store Cases
Valid Missing
N Percent N Percent
Knowledge of the company
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
Satisfacition with the
company
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
Preference for the company
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
Purchase Intention for the
products
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%

Loyalty for the company
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
Would recommend company
to a friend
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Not often 100 96.2% 4 3.8%
Moderate 204 100.0% 0 0.0%
Often 284 100.0% 0 0.0%
Case Processing Summary
How often they visit the store Cases
Total
N Percent
Knowledge of the company
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
Satisfacition with the company
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
Preference for the company
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
Purchase Intention for the products Not often 104 100.0%

Moderate 204 100.0%
Often 284 100.0%
Loyalty for the company
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
Would recommend company to a friend
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
COMPUTE
CustomerAttitude=Knowledge +
Satisfaction + Preference +
Purchase_intentions + Loyalty +
Recommend
Not often 104 100.0%
Moderate 204 100.0%
Often 284 100.0%
Descriptives
How often they visit the store Statistic Std. Error
Knowledge of the company Not often Mean 2.76 .169
95% Confidence Interval for
Mean
Lower Bound 2.42
Upper Bound 3.10
5% Trimmed Mean 2.68
Median 2.00
Variance 2.851
Std. Deviation 1.688
Minimum 1

Maximum 6
Range 5
Interquartile Range 3
Skewness .644 .241
Kurtosis -.750 .478
Moderate
Mean 4.90 .116
95% Confidence Interval for
Mean
Lower Bound 4.67
Upper Bound 5.13
5% Trimmed Mean 4.95
Median 6.00
Variance 2.769
Std. Deviation 1.664
Minimum 2
Maximum 7
Range 5
Interquartile Range 2
Skewness -.750 .170
Kurtosis -.808 .339
Often Mean 6.45 .036
95% Confidence Interval for
Mean
Lower Bound 6.38
Upper Bound 6.52
5% Trimmed Mean 6.50
Median 7.00

Variance .362
Std. Deviation .601
Descriptives
How often they visit the store Statistic Std. Error
Knowledge of the company Often Minimum 5
Maximum 7
Range 2
Interquartile Range 1
Skewness -.595 .145
Kurtosis -.572 .288
Satisfacition with the
company
Not often Mean 2.40 .094
95% Confidence Interval for
Mean
Lower Bound 2.21
Upper Bound 2.59
5% Trimmed Mean 2.39
Median 3.00
Variance .889
Std. Deviation .943
Minimum 1
Maximum 4
Range 3
Interquartile Range 1
Skewness -.295 .241
Kurtosis -1.037 .478

Moderate
Mean 5.39 .072
95% Confidence Interval for
Mean
Lower Bound 5.25
Upper Bound 5.53
5% Trimmed Mean 5.46
Median 6.00
Variance 1.067
Std. Deviation 1.033
Minimum 2
Maximum 7
Range 5
Interquartile Range 1
Skewness -1.061 .170
Kurtosis 1.358 .339
Often Mean 6.07 .046
Descriptives
How often they visit the store Statistic Std. Error
Satisfacition with the
company
Often 95% Confidence Interval for
Mean
Lower Bound 5.98
Upper Bound 6.16
5% Trimmed Mean 6.16
Median 6.00
Variance .603
Std. Deviation .776
Minimum 2

Maximum 7
Range 5
Interquartile Range 0
Skewness -2.313 .145
Kurtosis 9.966 .288
Preference for the company
Not often
Mean 2.36 .145
95% Confidence Interval for
Mean
Lower Bound 2.07
Upper Bound 2.65
5% Trimmed Mean 2.23
Median 2.00
Variance 2.091
Std. Deviation 1.446
Minimum 1
Maximum 6
Range 5
Interquartile Range 2
Skewness 1.144 .241
Kurtosis .808 .478
Moderate Mean 2.82 .110
95% Confidence Interval for
Mean
Lower Bound 2.61
Upper Bound 3.04
5% Trimmed Mean 2.80
Median 3.00
Variance 2.471

Std. Deviation 1.572
Minimum 1
Descriptives
How often they visit the store Statistic Std. Error
Preference for the company Moderate Maximum 5
Range 4
Interquartile Range 3
Skewness .050 .170
Kurtosis -1.561 .339
Often
Mean 4.45 .098
95% Confidence Interval for
Mean
Lower Bound 4.26
Upper Bound 4.64
5% Trimmed Mean 4.50
Median 5.00
Variance 2.736
Std. Deviation 1.654
Minimum 1
Maximum 7
Range 6
Interquartile Range 3
Skewness -.517 .145
Kurtosis -.669 .288

Purchase Intention for the
products
Not often
Mean 4.16 .184
95% Confidence Interval for
Mean
Lower Bound 3.80
Upper Bound 4.52
5% Trimmed Mean 4.18
Median 4.00
Variance 3.368
Std. Deviation 1.835
Minimum 1
Maximum 7
Range 6
Interquartile Range 3
Skewness -.401 .241
Kurtosis -.771 .478
Moderate
Mean 4.04 .133
95% Confidence Interval for
Mean Lower Bound 3.78
Descriptives
How often they visit the store Statistic Std. Error
Purchase Intention for the
products
Moderate 95% Confidence Interval for
Mean Upper Bound 4.30
5% Trimmed Mean 3.99
Median 4.00
Variance 3.585
Std. Deviation 1.893

Minimum 2
Maximum 7
Range 5
Interquartile Range 4
Skewness .383 .170
Kurtosis -1.409 .339
Often
Mean 5.01 .071
95% Confidence Interval for
Mean
Lower Bound 4.87
Upper Bound 5.15
5% Trimmed Mean 5.02
Median 5.00
Variance 1.427
Std. Deviation 1.195
Minimum 3
Maximum 7
Range 4
Interquartile Range 1
Skewness -.178 .145
Kurtosis -.523 .288
Loyalty for the company Not often Mean 3.96 .152
95% Confidence Interval for
Mean
Lower Bound 3.66
Upper Bound 4.26
5% Trimmed Mean 3.96

Median 5.00
Variance 2.301
Std. Deviation 1.517
Minimum 2
Maximum 6
Descriptives
How often they visit the store Statistic Std. Error
Loyalty for the company Not often Range 4
Interquartile Range 3
Skewness -.144 .241
Kurtosis -1.581 .478
Moderate Mean 4.14 .109
95% Confidence Interval for
Mean
Lower Bound 3.92
Upper Bound 4.35
5% Trimmed Mean 4.15
Median 5.00
Variance 2.444
Std. Deviation 1.563
Minimum 2
Maximum 6
Range 4
Interquartile Range 3

Skewness -.386 .170
Kurtosis -1.510 .339
Often
Mean 3.92 .093
95% Confidence Interval for
Mean
Lower Bound 3.73
Upper Bound 4.10
5% Trimmed Mean 3.85
Median 4.00
Variance 2.480
Std. Deviation 1.575
Minimum 2
Maximum 7
Range 5
Interquartile Range 3
Skewness .250 .145
Kurtosis -1.146 .288
Would recommend company
to a friend Not often
Mean 3.47 .114
95% Confidence Interval for
Mean
Lower Bound 3.24
Upper Bound 3.70
Descriptives
How often they visit the store Statistic Std. Error
Would recommend company
to a friend
Not often 5% Trimmed Mean 3.46
Median 3.00

Variance 1.302
Std. Deviation 1.141
Minimum 2
Maximum 6
Range 4
Interquartile Range 2
Skewness .117 .241
Kurtosis -1.247 .478
Moderate
Mean 4.92 .035
95% Confidence Interval for
Mean
Lower Bound 4.85
Upper Bound 4.98
5% Trimmed Mean 4.91
Median 5.00
Variance .244
Std. Deviation .494
Minimum 4
Maximum 6
Range 2
Interquartile Range 0
Skewness -.185 .170
Kurtosis .989 .339
Often Mean 6.35 .028
95% Confidence Interval for Lower Bound 6.29

Mean Upper Bound 6.40
5% Trimmed Mean 6.33
Median 6.00
Variance .228
Std. Deviation .477
Minimum 6
Maximum 7
Range 1
Descriptives
How often they visit the store Statistic Std. Error
Would recommend company
to a friend
Often Interquartile Range 1
Skewness .639 .145
Kurtosis -1.603 .288
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction +
Preference +
Purchase_intentions +
Loyalty + Recommend
Not often Mean 19.1100 .39310
95% Confidence Interval for
Mean
Lower Bound 18.3300
Upper Bound 19.8900
5% Trimmed Mean 19.2444
Median 19.0000
Variance 15.452
Std. Deviation 3.93096
Minimum 8.00
Maximum 26.00

Range 18.00
Interquartile Range 6.50
Skewness -.286 .241
Kurtosis .158 .478
Moderate
Mean 26.2108 .30100
95% Confidence Interval for
Mean
Lower Bound 25.6173
Upper Bound 26.8043
5% Trimmed Mean 26.1786
Median 26.5000
Variance 18.482
Std. Deviation 4.29912
Minimum 17.00
Maximum 35.00
Range 18.00
Interquartile Range 6.00
Skewness .076 .170
Kurtosis -.755 .339
Often
Mean 32.2500 .15637
95% Confidence Interval for
Mean
Lower Bound 31.9422
Upper Bound 32.5578
5% Trimmed Mean 32.2426
Descriptives
How often they visit the store Statistic Std. Error

COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction +
Preference +
Purchase_intentions +
Loyalty + Recommend
Often Median 32.0000
Variance 6.944
Std. Deviation 2.63521
Minimum 26.00
Maximum 38.00
Range 12.00
Interquartile Range 4.00
Skewness .013 .145
Kurtosis -.806 .288
ONEWAY CustomerAttitude BY User_Group
/STATISTICS DESCRIPTIVES HOMOGENEITY
/MISSING ANALYSIS.
Oneway
Notes
Output Created 06-OCT-2018 22:03:55

Comments
Input
Data
C:\Users\me\Desktop\
assignment\
2712879_1621455809_Custo
mer_attributes(1).sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 602
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.
Syntax
ONEWAY CustomerAttitude
BY User_Group
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.00
[DataSet1] C:\Users\me\Desktop\assignment\2712879_1621455809_Customer_attributes(1).sav

Descriptives
COMPUTE CustomerAttitude=Knowledge + Satisfaction + Preference + Purchase_intentions + Loyalty +
Recommend
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean
Lower Bound Upper Bound
Not often 104 19.5769 4.51468 .44270 18.6989 20.4549
Moderate 204 26.2108 4.29912 .30100 25.6173 26.8043
Often 284 32.2500 2.63521 .15637 31.9422 32.5578
Total 592 27.9426 5.95756 .24485 27.4617 28.4235
Descriptives
COMPUTE CustomerAttitude=Knowledge + Satisfaction + Preference + Purchase_intentions + Loyalty +
Recommend
Minimum Maximum
Not often 8.00 32.00
Moderate 17.00 35.00
Often 26.00 38.00
Total 8.00 38.00
Test of Homogeneity of Variances

COMPUTE CustomerAttitude=Knowledge +
Satisfaction + Preference + Purchase_intentions +
Loyalty + Recommend
Levene Statistic df1 df2 Sig.
33.127 2 589 .000
ANOVA
COMPUTE CustomerAttitude=Knowledge + Satisfaction + Preference + Purchase_intentions
+ Loyalty + Recommend
Sum of Squares df Mean Square F Sig.
Between Groups 13159.476 2 6579.738 495.801 .000
Within Groups 7816.571 589 13.271
Total 20976.047 591
ONEWAY CustomerAttitude Knowledge Satisfaction Preference Purchase_intentions Loyalty
Recommend BY User_Group
/STATISTICS DESCRIPTIVES HOMOGENEITY
/MISSING ANALYSIS.
Oneway

Notes
Output Created 06-OCT-2018 22:04:44
Comments
Input
Data
C:\Users\me\Desktop\
assignment\
2712879_1621455809_Custo
mer_attributes(1).sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 602
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.

Syntax
ONEWAY CustomerAttitude
Knowledge Satisfaction
Preference
Purchase_intentions Loyalty
Recommend BY User_Group
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
[DataSet1] C:\Users\me\Desktop\assignment\2712879_1621455809_Customer_attributes(1).sav
Descriptives
N Mean Std. Deviation Std. Error 95% Confidence
Interval for
Mean
Lower Bound
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction +
Preference +
Purchase_intentions +
Loyalty + Recommend
Not often 104 19.5769 4.51468 .44270 18.6989
Moderate 204 26.2108 4.29912 .30100 25.6173
Often 284 32.2500 2.63521 .15637 31.9422
Total 592 27.9426 5.95756 .24485 27.4617

Knowledge of the company
Not often 104 2.85 1.711 .168 2.51
Moderate 204 4.90 1.664 .116 4.67
Often 284 6.45 .601 .036 6.38
Total 592 5.28 1.840 .076 5.14
Satisfacition with the
company
Not often 104 2.54 1.157 .113 2.31
Moderate 204 5.39 1.033 .072 5.25
Often 284 6.07 .776 .046 5.98
Total 592 5.22 1.585 .065 5.09
Preference for the company
Not often 104 2.46 1.507 .148 2.17
Moderate 204 2.82 1.572 .110 2.61
Often 284 4.45 1.654 .098 4.26
Total 592 3.54 1.826 .075 3.39
Purchase Intention for the
products
Not often 100 4.16 1.835 .184 3.80
Moderate 204 4.04 1.893 .133 3.78
Often 284 5.01 1.195 .071 4.87
Total 588 4.53 1.648 .068 4.40
Loyalty for the company
Not often 104 3.92 1.499 .147 3.63
Moderate 204 4.14 1.563 .109 3.92
Often 284 3.92 1.575 .093 3.73
Total 592 3.99 1.559 .064 3.87
Would recommend company
to a friend
Not often 104 3.46 1.131 .111 3.24
Moderate 204 4.92 .494 .035 4.85
Often 284 6.35 .477 .028 6.29
Total 592 5.35 1.260 .052 5.25

Descriptives
95% Confidence
Interval for Mean
Minimum Maximum
Upper Bound
COMPUTE
CustomerAttitude=Knowledge +
Satisfaction + Preference +
Purchase_intentions + Loyalty +
Recommend
Not often 20.4549 8.00 32.00
Moderate 26.8043 17.00 35.00
Often 32.5578 26.00 38.00
Total 28.4235 8.00 38.00
Knowledge of the company
Not often 3.18 1 6
Moderate 5.13 2 7
Often 6.52 5 7
Total 5.43 1 7
Satisfacition with the company
Not often 2.76 1 6
Moderate 5.53 2 7
Often 6.16 2 7
Total 5.34 1 7
Preference for the company
Not often 2.75 1 6
Moderate 3.04 1 5
Often 4.64 1 7
Total 3.69 1 7
Purchase Intention for the products
Not often 4.52 1 7
Moderate 4.30 2 7
Often 5.15 3 7
Total 4.66 1 7

Loyalty for the company
Not often 4.21 2 6
Moderate 4.35 2 6
Often 4.10 2 7
Total 4.12 2 7
Would recommend company to a
friend
Not often 3.68 2 6
Moderate 4.98 4 6
Often 6.40 6 7
Total 5.45 2 7
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
33.127 2 589 .000
Knowledge of the company 137.679 2 589 .000
Satisfacition with the
company 34.012 2 589 .000
Preference for the company 3.007 2 589 .050
Purchase Intention for the
products 51.499 2 585 .000
Loyalty for the company .438 2 589 .645
Would recommend company
to a friend 155.697 2 589 .000

ANOVA
Sum of Squares df Mean Square F
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Between Groups 13159.476 2 6579.738 495.801
Within Groups 7816.571 589 13.271
Total 20976.047 591
Knowledge of the company
Between Groups 1034.437 2 517.218 315.401
Within Groups 965.888 589 1.640
Total 2000.324 591
Satisfacition with the
company
Between Groups 959.259 2 479.630 538.032
Within Groups 525.065 589 .891
Total 1484.324 591
Preference for the company
Between Groups 461.224 2 230.612 89.966
Within Groups 1509.803 589 2.563
Total 1971.027 591
Purchase Intention for the
products
Between Groups 129.379 2 64.690 25.830
Within Groups 1465.070 585 2.504
Total 1594.449 587
Loyalty for the company
Between Groups 6.460 2 3.230 1.331
Within Groups 1429.513 589 2.427
Total 1435.973 591
Would recommend company
to a friend
Between Groups 692.399 2 346.199 829.181
Within Groups 245.919 589 .418

Total 938.318 591
ANOVA
Sig.
COMPUTE CustomerAttitude=Knowledge +
Satisfaction + Preference + Purchase_intentions +
Loyalty + Recommend
Between Groups .000
Within Groups
Total
Knowledge of the company
Between Groups .000
Within Groups
Total
Satisfacition with the company
Between Groups .000
Within Groups
Total
Preference for the company
Between Groups .000
Within Groups
Total
Purchase Intention for the products
Between Groups .000
Within Groups
Total
Loyalty for the company
Between Groups .265
Within Groups
Total
Would recommend company to a friend Between Groups .000
Within Groups

Total
EXAMINE VARIABLES=Knowledge Satisfaction Preference Purchase_intentions Loyalty Recommend
CustomerAttitude BY Gender
/PLOT NONE
/STATISTICS DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Explore
Notes
Output Created 06-OCT-2018 22:05:24
Comments
Input
Data
C:\Users\me\Desktop\
assignment\
2712879_1621455809_Custo
mer_attributes(1).sav
Active Dataset DataSet1

Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 602
Missing Value Handling
Definition of Missing
User-defined missing values
for dependent variables are
treated as missing.
Cases Used
Statistics are based on cases
with no missing values for
any dependent variable or
factor used.
Syntax
EXAMINE
VARIABLES=Knowledge
Satisfaction Preference
Purchase_intentions Loyalty
Recommend
CustomerAttitude BY Gender
/PLOT NONE
/STATISTICS
DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
[DataSet1] C:\Users\me\Desktop\assignment\2712879_1621455809_Customer_attributes(1).sav

Gender
Case Processing Summary
Gender Cases
Valid Missing Total
N Percent N Percent N Percent
Knowledge of the company
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Satisfacition with the
company
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Preference for the company
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Purchase Intention for the
products
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Loyalty for the company
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Would recommend company Female 388 100.0% 0 0.0% 388 100.0%

to a friend Male 200 98.0% 4 2.0% 204 100.0%
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction +
Preference +
Purchase_intentions +
Loyalty + Recommend
Female 388 100.0% 0 0.0% 388 100.0%
Male 200 98.0% 4 2.0% 204 100.0%
Descriptives
Gender Statistic Std. Error
Knowledge of the company
Female
Mean 5.02 .100
95% Confidence Interval for
Mean
Lower Bound 4.82
Upper Bound 5.22
5% Trimmed Mean 5.13
Median 6.00
Variance 3.845
Std. Deviation 1.961
Minimum 1
Maximum 7
Range 6
Interquartile Range 2
Skewness -.823 .124
Kurtosis -.765 .247
Male Mean 5.80 .104

95% Confidence Interval for
Mean
Lower Bound 5.59
Upper Bound 6.01
5% Trimmed Mean 5.99
Median 6.00
Variance 2.171
Std. Deviation 1.473
Minimum 1
Maximum 7
Range 6
Interquartile Range 1
Skewness -1.821 .172
Kurtosis 3.120 .342
Satisfacition with the
company Female
Mean 5.18 .081
95% Confidence Interval for
Mean
Lower Bound 5.02
Upper Bound 5.33
5% Trimmed Mean 5.31
Median 6.00
Variance 2.543
Std. Deviation 1.595
Descriptives
Gender Statistic Std. Error
Satisfacition with the Female Minimum 1

company Maximum 7
Range 6
Interquartile Range 1
Skewness -1.212 .124
Kurtosis .573 .247
Male
Mean 5.28 .112
95% Confidence Interval for
Mean
Lower Bound 5.06
Upper Bound 5.50
5% Trimmed Mean 5.39
Median 6.00
Variance 2.494
Std. Deviation 1.579
Minimum 1
Maximum 7
Range 6
Interquartile Range 1
Skewness -1.151 .172
Kurtosis .256 .342
Preference for the company Female Mean 3.19 .095
95% Confidence Interval for
Mean
Lower Bound 3.00
Upper Bound 3.37
5% Trimmed Mean 3.13
Median 3.00

Variance 3.521
Std. Deviation 1.876
Minimum 1
Maximum 7
Range 6
Interquartile Range 4
Skewness .257 .124
Kurtosis -1.228 .247
Male Mean 4.20 .108
Descriptives
Gender Statistic Std. Error
Preference for the company Male 95% Confidence Interval for
Mean
Lower Bound 3.99
Upper Bound 4.41
5% Trimmed Mean 4.22
Median 4.00
Variance 2.332
Std. Deviation 1.527
Minimum 1
Maximum 7
Range 6
Interquartile Range 2
Skewness -.240 .172
Kurtosis -.791 .342

Purchase Intention for the
products
Female
Mean 4.67 .082
95% Confidence Interval for
Mean
Lower Bound 4.51
Upper Bound 4.83
5% Trimmed Mean 4.72
Median 5.00
Variance 2.620
Std. Deviation 1.619
Minimum 1
Maximum 7
Range 6
Interquartile Range 2
Skewness -.455 .124
Kurtosis -.606 .247
Male
Mean 4.26 .118
95% Confidence Interval for
Mean
Lower Bound 4.03
Upper Bound 4.49
5% Trimmed Mean 4.26
Median 4.00
Variance 2.806
Std. Deviation 1.675
Minimum 1
Descriptives
Gender Statistic Std. Error

Purchase Intention for the
products
Male Maximum 7
Range 6
Interquartile Range 3
Skewness .126 .172
Kurtosis -1.100 .342
Loyalty for the company
Female
Mean 3.44 .081
95% Confidence Interval for
Mean
Lower Bound 3.28
Upper Bound 3.60
5% Trimmed Mean 3.35
Median 3.00
Variance 2.521
Std. Deviation 1.588
Minimum 2
Maximum 7
Range 5
Interquartile Range 3
Skewness .711 .124
Kurtosis -.901 .247
Male Mean 5.08 .051
95% Confidence Interval for
Mean
Lower Bound 4.98
Upper Bound 5.18
5% Trimmed Mean 5.12
Median 5.00
Variance .516

Std. Deviation .718
Minimum 3
Maximum 7
Range 4
Interquartile Range 0
Skewness -.777 .172
Kurtosis 2.984 .342
Would recommend company
to a friend Female
Mean 5.40 .064
95% Confidence Interval for
Mean Lower Bound 5.28
Descriptives
Gender Statistic Std. Error
Would recommend company
to a friend
Female 95% Confidence Interval for
Mean Upper Bound 5.53
5% Trimmed Mean 5.49
Median 6.00
Variance 1.574
Std. Deviation 1.255
Minimum 2
Maximum 7
Range 5
Interquartile Range 1
Skewness -.873 .124

Kurtosis .520 .247
Male
Mean 5.29 .088
95% Confidence Interval for
Mean
Lower Bound 5.11
Upper Bound 5.46
5% Trimmed Mean 5.37
Median 5.00
Variance 1.541
Std. Deviation 1.242
Minimum 2
Maximum 7
Range 5
Interquartile Range 1
Skewness -.891 .172
Kurtosis .701 .342
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Female
Mean 26.8969 .30243
95% Confidence Interval for
Mean
Lower Bound 26.3023
Upper Bound 27.4915
5% Trimmed Mean 27.1409
Median 28.0000
Variance 35.488
Std. Deviation 5.95718
Minimum 8.00
Maximum 37.00

Descriptives
Gender Statistic Std. Error
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Female Range 29.00
Interquartile Range 9.00
Skewness -.624 .124
Kurtosis -.383 .247
Male
Mean 29.9050 .38831
95% Confidence Interval for
Mean
Lower Bound 29.1393
Upper Bound 30.6707
5% Trimmed Mean 30.2111
Median 31.0000
Variance 30.157
Std. Deviation 5.49152
Minimum 15.00
Maximum 38.00
Range 23.00
Interquartile Range 7.00
Skewness -.762 .172
Kurtosis -.184 .342
ONEWAY CustomerAttitude Knowledge Satisfaction Preference Purchase_intentions Loyalty
Recommend BY Gender
/STATISTICS DESCRIPTIVES HOMOGENEITY
/MISSING ANALYSIS.

Oneway
Notes
Output Created 06-OCT-2018 22:05:38
Comments
Input
Data
C:\Users\me\Desktop\
assignment\
2712879_1621455809_Custo
mer_attributes(1).sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 602
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.

Syntax
ONEWAY CustomerAttitude
Knowledge Satisfaction
Preference
Purchase_intentions Loyalty
Recommend BY Gender
/STATISTICS
DESCRIPTIVES
HOMOGENEITY
/MISSING ANALYSIS.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.02
[DataSet1] C:\Users\me\Desktop\assignment\2712879_1621455809_Customer_attributes(1).sav
Descriptives
N Mean Std. Deviation Std. Error 95% Confidence
Interval for Mean
Lower Bound
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Female 388 26.8969 5.95718 .30243 26.3023
Male 204 29.9314 5.44160 .38099 29.1802
Total 592 27.9426 5.95756 .24485 27.4617
Knowledge of the company Female 388 5.02 1.961 .100 4.82
Male 204 5.78 1.463 .102 5.58

Total 592 5.28 1.840 .076 5.14
Satisfacition with the
company
Female 388 5.18 1.595 .081 5.02
Male 204 5.29 1.567 .110 5.08
Total 592 5.22 1.585 .065 5.09
Preference for the company
Female 388 3.19 1.876 .095 3.00
Male 204 4.22 1.516 .106 4.01
Total 592 3.54 1.826 .075 3.39
Purchase Intention for the
products
Female 388 4.67 1.619 .082 4.51
Male 200 4.26 1.675 .118 4.03
Total 588 4.53 1.648 .068 4.40
Loyalty for the company
Female 388 3.44 1.588 .081 3.28
Male 204 5.04 .768 .054 4.93
Total 592 3.99 1.559 .064 3.87
Would recommend company
to a friend
Female 388 5.40 1.255 .064 5.28
Male 204 5.25 1.267 .089 5.07
Total 592 5.35 1.260 .052 5.25
Descriptives
95% Confidence
Interval for Mean
Minimum Maximum
Upper Bound
COMPUTE
CustomerAttitude=Knowledge +
Satisfaction + Preference +
Purchase_intentions + Loyalty +
Recommend
Female 27.4915 8.00 37.00
Male 30.6826 15.00 38.00
Total 28.4235 8.00 38.00

Knowledge of the company
Female 5.22 1 7
Male 5.99 1 7
Total 5.43 1 7
Satisfacition with the company
Female 5.33 1 7
Male 5.51 1 7
Total 5.34 1 7
Preference for the company
Female 3.37 1 7
Male 4.42 1 7
Total 3.69 1 7
Purchase Intention for the products
Female 4.83 1 7
Male 4.49 1 7
Total 4.66 1 7
Loyalty for the company
Female 3.60 2 7
Male 5.15 3 7
Total 4.12 2 7
Would recommend company to a friend
Female 5.53 2 7
Male 5.42 2 7
Total 5.45 2 7
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.

COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
5.088 1 590 .024
Knowledge of the company 56.606 1 590 .000
Satisfacition with the
company .024 1 590 .877
Preference for the company 14.901 1 590 .000
Purchase Intention for the
products 2.724 1 586 .099
Loyalty for the company 246.135 1 590 .000
Would recommend company
to a friend .157 1 590 .692
ANOVA
Sum of Squares df Mean Square F
COMPUTE
CustomerAttitude=Knowledg
e + Satisfaction + Preference
+ Purchase_intentions +
Loyalty + Recommend
Between Groups 1231.132 1 1231.132 36.788
Within Groups 19744.916 590 33.466
Total 20976.047 591
Knowledge of the company
Between Groups 77.979 1 77.979 23.933
Within Groups 1922.345 590 3.258
Total 2000.324 591
Satisfacition with the
company
Between Groups 1.889 1 1.889 .752
Within Groups 1482.435 590 2.513

Total 1484.324 591
Preference for the company
Between Groups 141.878 1 141.878 45.763
Within Groups 1829.149 590 3.100
Total 1971.027 591
Purchase Intention for the
products
Between Groups 22.196 1 22.196 8.273
Within Groups 1572.253 586 2.683
Total 1594.449 587
Loyalty for the company
Between Groups 340.534 1 340.534 183.411
Within Groups 1095.439 590 1.857
Total 1435.973 591
Would recommend company
to a friend
Between Groups 3.294 1 3.294 2.079
Within Groups 935.023 590 1.585
Total 938.318 591
ANOVA
Sig.
COMPUTE CustomerAttitude=Knowledge +
Satisfaction + Preference + Purchase_intentions +
Loyalty + Recommend
Between Groups .000
Within Groups
Total
Knowledge of the company
Between Groups .000
Within Groups
Total
Satisfacition with the company Between Groups .386
Within Groups

Total
Preference for the company
Between Groups .000
Within Groups
Total
Purchase Intention for the products
Between Groups .004
Within Groups
Total
Loyalty for the company
Between Groups .000
Within Groups
Total
Would recommend company to a friend
Between Groups .150
Within Groups
Total
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=website_vs_instore SUM(Profit_Total)
[name="SUM_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: website_vs_instore=col(source(s), name("website_vs_instore"), unit.category())
DATA: SUM_Profit_Total=col(source(s), name("SUM_Profit_Total"))
GUIDE: axis(dim(1), label("Website or instore location of products"))
GUIDE: axis(dim(2), label("Sum Profit Total"))

SCALE: cat(dim(1), include("1.00", "2.00"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(website_vs_instore*SUM_Profit_Total), shape.interior(shape.square))
END GPL.
GGraph
Notes
Output Created 06-OCT-2018 22:08:18
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034

Notes
Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=website_vs_inst
ore SUM(Profit_Total)
[name="SUM_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
website_vs_instore=col(sourc
e(s),
name("website_vs_instore"),
unit.category())
DATA:
SUM_Profit_Total=col(source
(s),
name("SUM_Profit_Total"))
GUIDE: axis(dim(1),
label("Website or instore
location of products"))
GUIDE: axis(dim(2),
label("Sum Profit Total"))
SCALE: cat(dim(1),
include("1.00", "2.00"))
SCALE: linear(dim(2),

include(0))
ELEMENT:
interval(position(website_vs_i
nstore*SUM_Profit_Total),
shape.interior(shape.square))
END GPL.
Resources
Processor Time 00:00:00.20
Elapsed Time 00:00:00.19
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=website_vs_instore MEAN(Profit_Total)
[name="MEAN_Profit_Total"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: website_vs_instore=col(source(s), name("website_vs_instore"), unit.category())
DATA: MEAN_Profit_Total=col(source(s), name("MEAN_Profit_Total"))
GUIDE: axis(dim(1), label("Website or instore location of products"))
GUIDE: axis(dim(2), label("Mean Profit Total"))
SCALE: cat(dim(1), include("1.00", "2.00"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(website_vs_instore*MEAN_Profit_Total), shape.interior(shape.square))
END GPL.
GGraph

Notes
Output Created 06-OCT-2018 22:08:55
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Notes
Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=website_vs_inst
ore MEAN(Profit_Total)
[name="MEAN_Profit_Total"]
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE:
s=userSource(id("graphdatas
et"))
DATA:
website_vs_instore=col(sourc

e(s),
name("website_vs_instore"),
unit.category())
DATA:
MEAN_Profit_Total=col(sourc
e(s),
name("MEAN_Profit_Total"))
GUIDE: axis(dim(1),
label("Website or instore
location of products"))
GUIDE: axis(dim(2),
label("Mean Profit Total"))
SCALE: cat(dim(1),
include("1.00", "2.00"))
SCALE: linear(dim(2),
include(0))
ELEMENT:
interval(position(website_vs_i
nstore*MEAN_Profit_Total),
shape.interior(shape.square))
END GPL.
Resources
Processor Time 00:00:00.23
Elapsed Time 00:00:00.20
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

ONEWAY Profit_Total BY website_vs_instore
/MISSING ANALYSIS.
Oneway
Notes

Output Created 06-OCT-2018 22:09:25
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics for each analysis
are based on cases with no
missing data for any variable
in the analysis.
Syntax
ONEWAY Profit_Total BY
website_vs_instore
/MISSING ANALYSIS.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.00
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

ANOVA
Profit Total
Sum of Squares df Mean Square F Sig.
Between Groups 9082.599 1 9082.599 532.694 .000
Within Groups 10400.690 610 17.050
Total 19483.289 611
SORT CASES BY website_vs_instore.
SPLIT FILE SEPARATE BY website_vs_instore.
FREQUENCIES VARIABLES=Profit_Total
/FORMAT=NOTABLE
/STATISTICS=STDDEV MEAN
/ORDER=ANALYSIS.
Frequencies
Notes

Output Created 06-OCT-2018 22:10:53
Comments
Input
Data
C:\Users\me\Desktop\
assignment\Shop sales
including website.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File Website or instore location of
products
N of Rows in Working Data
File 1034
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used Statistics are based on all
cases with valid data.
Syntax
FREQUENCIES
VARIABLES=Profit_Total
/FORMAT=NOTABLE
/STATISTICS=STDDEV
MEAN
/ORDER=ANALYSIS.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.00
[DataSet1] C:\Users\me\Desktop\assignment\Shop sales including website.sav

Website or instore location of products = .
Statisticsa
Profit Total
N
Valid 0
Missing 422
a. Website or instore location of
products = .
Website or instore location of products = website
Statisticsa
Profit Total

N
Valid 27
Missing 0
Mean 25.0000
Std. Deviation .00000
a. Website or instore location of
products = website
Website or instore location of products = In store
Statisticsa
Profit Total
N
Valid 585
Missing 0
Mean 6.2405
Std. Deviation 4.22012
a. Website or instore location of
products = In store