Cell Inter Spss

8/3/2019 Cell Inter Spss

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GETFILE='C:\Users\Dell\Downloads\Cell_Inter.sav'.

DATASET NAME DataSet1 WINDOW=FRONT.QUICK CLUSTERfunused0 funused1 funused2 funused3 funused4 funused5 funused6 funused7funused8 funused9/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(10) CONVERGE(0)/METHOD=KMEANS(NOUPDATE)/PRINT INITIAL.

Initial Cluster Centers

Cluster

1 2 3

SMS 1 1 2

Alarm1 1 2

Camera 1 2 1

Scheduler 1 2 2

Music / Radio Playback 1 2 2

Games 1 2 2

Internet 1 2 1

Time and Date 1 2 2

Download 1 2 1

Other 2 2 1

Iteration History(a)

IterationChange in Cluster Centers

1 2 3

1 .906 1.112 .800

2 .028 .081 .552

3 .023 .062 .247

4 .000 .041 .225

5 .000 .093 .340

6 .046 .044 .220

7 .097 .022 .273

8 .030 .040 .086

9 .075 .036 .155

10.021 .019 .060

a Iterations stopped because the maximum number of iterations was performed. Iterations failed to converge. Themaximum absolute coordinate change for any center is .034. The current iteration is 10. The minimum distancebetween initial centers is 2.449.

Final Cluster Centers


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Cluster

1 2 3

SMS 1 1 1

Alarm 1 1 2

Camera 2 2 1

Scheduler 1 2 2


Games 1 1 1

Internet 2 2 2

Time and Date 1 1 2

Download 1 2 1

Other 2 2 2

Number of Cases in each Cluster

Cluster 1 86.000

2 73.000

3 47.000

Valid 206.000

Missing .000

QUICK CLUSTERfunused0 funused1 funused2 funused3 funused4 funused5 funused6 funused7funused8 funused9/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(20) CONVERGE(0)/METHOD=KMEANS(NOUPDATE)

/PRINT INITIAL.

Quick Cluster


Cluster

1 2 3

SMS 1 1 2

Alarm 1 1 2

Camera 1 2 1

Scheduler 1 2 2Music / Radio Playback 1 2 2

Games 1 2 2

Internet 1 2 1

Time and Date 1 2 2

Download 1 2 1

Other 2 2 1


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Iteration

Change in Cluster Centers

1 2 3

1 .906 1.112 .800

2 .028 .081 .552

3 .023 .062 .2474 .000 .041 .225

5 .000 .093 .340

6 .046 .044 .220

7 .097 .022 .273

8 .030 .040 .086

9 .075 .036 .155

10 .021 .019 .060

11 .000 .000 .000

a Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change forany center is .000. The current iteration is 11. The minimum distance between initial centers is 2.449.


Cluster

1 2 3

SMS 1 1 1

Alarm 1 1 2

Camera 2 2 1

Scheduler 1 2 2


Games 1 1 1

Internet 2 2 2

Time and Date 1 1 2

Download 1 2 1

Other 2 2 2


Cluster 1 86.000

2 73.000

3 47.000

Valid 206.000

Missing .000

QUICK CLUSTERfunused0 funused1 funused2 funused3 funused4 funused5 funused6 funused7funused8 funused9/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(20) CONVERGE(0)/METHOD=KMEANS(NOUPDATE)/SAVE CLUSTER/PRINT INITIAL.


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qqqqqq


Cluster

1 2 3

SMS 1 1 2

Alarm 1 1 2

Camera 1 2 1

Scheduler 1 2 2


Games 1 2 2

Internet 1 2 1

Time and Date 1 2 2

Download 1 2 1

Other 2 2 1


Iteration


1 2 3

1 .906 1.112 .800

2 .028 .081 .552

3 .023 .062 .247

4 .000 .041 .225

5 .000 .093 .340

6 .046 .044 .220

7 .097 .022 .273

8 .030 .040 .086

9 .075 .036 .155

10 .021 .019 .060

11 .000 .000 .000



Cluster

1 2 3

SMS 1 1 1

Alarm 1 1 2

Camera 2 2 1

Scheduler 1 2 2Music / Radio Playback 1 2 2

Games 1 1 1

Internet 2 2 2

Time and Date 1 1 2

Download 1 2 1

Other 2 2 2


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Cluster 1 86.000

2 73.000

3 47.000

Valid 206.000

Missing .000

USE ALL.COMPUTE filter_$=(QCL_1 = 3).VARIABLE LABEL filter_$ 'QCL_1 = 3 (FILTER)'.VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.FORMAT filter_$ (f1.0).FILTER BY filter_$.EXECUTE .FREQUENCIESVARIABLES=gender educatn service contype chrgfreq paymode/ORDER= ANALYSIS .

Frequencies

Statistics

Gender of

respondentLevel of

education

Name ofcurrentserviceprovider

ConnectionType

MonthlyRechargefrequency

Mode ofpayment

N Valid 47 47 47 47 47 47

Missing 0 0 0 0 0 0

Frequency TableGender of respondent

Frequency Percent Valid PercentCumulative

Percent

Valid Male 40 85.1 85.1 85.1

Female 7 14.9 14.9 100.0

Total 47 100.0 100.0

Level of education

Frequency Percent Valid Percent

Cumulative

Percent

Valid Class IX 1 2.1 2.1 2.1

Class XI/Inter 1 18 38.3 38.3 40.4

Class XII/Inter 2 28 59.6 59.6 100.0

Total 47 100.0 100.0

Name of current service provider


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Percent

Valid Airtel 12 25.5 25.5 25.5

BSNL 8 17.0 17.0 42.6

Hutch 13 27.7 27.7 70.2

Reliance 3 6.4 6.4 76.6

Tata Indicom 11 23.4 23.4 100.0

Total 47 100.0 100.0

Connection Type


Percent

Valid Prepaid 42 89.4 89.4 89.4

Post Paid 5 10.6 10.6 100.0

Total 47 100.0 100.0

Monthly Recharge frequency


Percent

Valid Less than once 3 6.4 6.4 6.4

Once 41 87.2 87.2 93.6

Twice 2 4.3 4.3 97.9

Three or more 1 2.1 2.1 100.0

Total 47 100.0 100.0

Mode of payment

Frequency Percent Valid Percent

Cumulative

PercentValid Cash 45 95.7 95.7 95.7

Cheque 1 2.1 2.1 97.9

Instrument 1 2.1 2.1 100.0

Total 47 100.0 100.0

QUICK CLUSTERmntspend billsms billothr billtalk billfix/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(10) CONVERGE(0)/METHOD=KMEANS(NOUPDATE)/PRINT INITIAL.

Quick ClusterInitial Cluster Centers

Cluster

1 2 3

Monthly expenditure onphone 1000.00 2000.00 99.00


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SMS bill 25.00 40.00 25.00

Other charges .00 .00 .00

Voice calls bill 75.00 60.00 75.00

Fixed component of bill 25.00 70.00 50.00


Iteration


1 2 3

1 252.234 .000 216.653

2 .000 .000 .000



Cluster

1 2 3


SMS bill 35.00 40.00 27.26

Other charges 2.50 .00 3.95




Cluster 1 4.000

2 1.000

3 42.000

Valid 47.000

Missing .000

// Begin here

Clusters based on monthly expenditure on phone is made using K-means method.

The following clusters are obtained. Cluster 2 has the maximum expenditure.

FILTER OFF.USE ALL.EXECUTE .QUICK CLUSTER

mntspend billsms billothr billtalk billfix/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(10) CONVERGE(0)/METHOD=KMEANS(NOUPDATE)/PRINT INITIAL.

Quick Cluster[DataSet1] C:\Users\Dell\Downloads\Cell_Inter.sav



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Cluster

1 2 3


SMS bill 25.00 40.00 10.00





Iteration


1 2 3

1 267.544 .000 225.437

2 .000 .000 .000



Cluster

1 2 3


SMS bill 27.85 40.00 26.65

Other charges 3.08 .00 5.77



It reduced the distance between the clusters centers after the maximum absolute coordinate change for any center is

obtained as .000. Now the three clusters with the changed mean value are obtained as above.


Cluster 1 13.000

2 1.000

3 192.000

Valid 206.000

Missing .000

VARIABLES=mntspend /COMPARE VARIABLE/PLOT=BOXPLOT/STATISTICS=NONE/NOTOTAL/MISSING=LISTWISE .

It could be seen that some cases are outliers and extremes, to make theconsistent clusters these were removed. A boxplot was made to see this.

ExploreCase Processing Summary

Cases

Valid Missing Total


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N Percent N Percent N Percent

Monthly expenditureon phone 206 100.0% 0 .0% 206 100.0%

Monthly expenditure on phone

2000.00

1500.00

1000.00

500.00

0.00

39

30

121

76

154

10435

81

134

277

89151

USE ALL.COMPUTE filter_$=(mntspend < 600).VARIABLE LABEL filter_$ 'mntspend < 600 (FILTER)'.VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.FORMAT filter_$ (f1.0).FILTER BY filter_$.EXECUTE .

QUICK CLUSTERmntspend billsms billothr billtalk billfix/MISSING=LISTWISE/CRITERIA= CLUSTER(3) MXITER(10) CONVERGE(0)

/METHOD=KMEANS(NOUPDATE)/PRINT INITIAL.The data was filtered and the filter criterion was the monthly expenditureless than Rs. 600 was kept and the expenditure above this was kept aside.

Quick ClusterInitial Cluster Centers

Cluster

1 2 3


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SMS bill .00 70.00 40.00





Iteration


1 2 3

1 71.530 72.482 97.952

2 12.762 7.535 19.970

3 .000 .000 .000



Cluster

1 2 3


SMS bill 27.66 31.48 22.57

Other charges 6.41 5.07 4.39



The above data tables were obtained on again taking out K-Means clusters. Out of 206 cases, 9 were kept aside.Number of Cases in each Cluster

Cluster 1 116.000

2 27.000

3 54.000

Valid 197.000

Missing .000

sort cases by QCL_2 (a) .SORT CASES BY QCL_2 .SPLIT FILELAYERED BY QCL_2 .

.The 9 extremes were manually kept in cluster 2 as it was the cluster of high

expenditure. And a new variable of clusters was added in the table. We pickthe cluster 2, from the above table it is clear that this group has thehighest expenditure on the phone.

CROSSTABS/TABLES=QCL_2 BY contype/FORMAT= AVALUE TABLES/CELLS= COUNT ROW COLUMN/COUNT ROUND CELL .


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Crosstabs[DataSet1] C:\Users\Dell\Downloads\Cell_Inter.sav

Case Processing Summary

Cases

Valid Missing Total


Cluster Number of Case *Connection Type 197 95.6% 9 4.4% 206 100.0%

Cluster Number of Case * Connection Type Crosstabulation

Connection Type Total

Prepaid Post Paid Prepaid

Cluster Numberof Case

1 Count 110 6 116

% within Cluster Number ofCase 94.8% 5.2% 100.0%

% within Connection Type 61.1% 35.3% 58.9%

2 Count 23 4 27



3 Count 47 7 54



Total Count 180 17 197



It is clear from the above table that the more people falling in cluster 2 have the Post paid connection as compared tothe other type of connection. Here by, we make an assumption that people having high expenditure on phone usuallyhave the post paid connection.CROSSTABS/TABLES=contype BY funused5/FORMAT= AVALUE TABLES/CELLS= COUNT ROW COLUMN/COUNT ROUND CELL .

Crosstabs

[DataSet1] C:\Users\Dell\Downloads\Cell_Inter.savCase Processing Summary

Cases

Valid Missing Total


ConnectionType * Games 206 100.0% 0 .0% 206 100.0%


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Connection Type * Games Crosstabulation

Games Total

Yes No Yes

ConnectionType

Prepaid Count 158 26 184


% within Games 88.8% 92.9% 89.3%Post Paid Count 20 2 22


% within Games 11.2% 7.1% 10.7%



% within Games 100.0% 100.0% 100.0%

The above cross tab shows that majority of post paid subscribers play games as compared to the pre paidconnection. Hence, it could be deducted that the cluster 2 people have high phone expenditure, prefer post paidplans and play games.CROSSTABS

/TABLES=QCL_2 BY funused0/FORMAT= AVALUE TABLES/CELLS= COUNT ROW COLUMN/COUNT ROUND CELL .

Crosstabs[DataSet1] C:\Users\Dell\Downloads\Cell_Inter.sav

Case Processing Summary

Cases

Valid Missing Total


Cluster Numberof Case * SMS 197 95.6% 9 4.4% 206 100.0%

Cluster Number of Case * SMS Crosstabulation

SMS Total

Yes No Yes

Cluster Numberof Case

1 Count 103 13 116

% within ClusterNumber of Case 88.8% 11.2% 100.0%

% within SMS 57.5% 72.2% 58.9%

2 Count 26 1 27


% within SMS 14.5% 5.6% 13.7%

3 Count 50 4 54


% within SMS 27.9% 22.2% 27.4%


% within ClusterNumber of Case

90.9% 9.1% 100.0%


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% within SMS 100.0% 100.0% 100.0%

The above cross tab shows that the cluster 2 people (96.3%) do use SMS service,

which is significantly higher than that of the pre paid users. Uptil here, we get that

CLUSTER 2 people spend the highest on the phone, like games and SMS services

and have postpaid plan.

Documents

Cell Inter Spss