Data Analysis for Travel Pac

The assignment involves analyzing data from travel industry to identify target segments and make recommendations for Travel Pac. The report analyzes the age group and traveling mode of male and female travelers, and presents findings that both males and females prefer to travel independently and like to send money on holiday trips by air. The report also identifies tools and techniques for decision making such as market research and feasibility analysis.

USING DATA

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Data analysis of Treval Pac, for the third Quester of 2017 over the travelling patters of
passengers in UK.........................................................................................................................1
The tools and techniques used for decision making of the Travel Pac.......................................3
Application of data method over the data...................................................................................3
Interpretation of data and presenting results...............................................................................7
Presenting recommendation to Travel Pac over a potential target segment..............................8
CONCLUSION................................................................................................................................8
REFERENCES..............................................................................................................................10

INTRODUCTION
The business organisations uses the techniques of data analysis to evaluate different
types of data and information gathered over a particular subject matter or topic. In this regard
data analysis can be defined as a process where by data is eventuated with use of different tools
and techniques and through bifurcating it under various variable to reach an outcome over the
data collected. In the present report the data is gathered is related with travellers who have
visiting UK for various purposes in third quarter of 2017 belong to various nations and having
distinct residential status. For this the data variables are analysed under which data is separated
under various categories and then the use of a statistical method and tool is done to reach an
effective outcome over the data. Also, the rules so reached are interpreted and for the
recommendation are present before reaching to final conclusion of the report.
MAIN BODY
Data analysis of Treval Pac, for the third Quester of 2017 over the travelling patters of
passengers in UK
The internal passenger Survey have collected data for a time period of 3 month that is
the third quarter of 2017 for the organisation Travel pac. The data is related with the travelling
pattern of people from the UK as well as outside UK. The data is gathered under disagreement
categorise such as age, sex, duration and others. Over all the data is presented under 14 distinct
categorises which gave different criteria of collection and gives a different understanding for
evaluation of the data (Silverman, 2018). The main heading and their sub headings are:
The year: is defined as a time frame of 12 months which starts from the month of
January with ending at December month this is the calender year which is different from fiscal
year. For the present year the year which is selected for data collection over the travelling
patterns of traveller in UK is 2017.
Quarter: is a time of 3 month in a year which means there are 4 quarter in a year. The
first quarter start from January and end n the moth on march and after these comes another three
quarter. The particular quarter fro which data is collected over the traveller in UK for year 2017
is the third quarter which starts in month of July and ends in September.
Age: category defines the age of the traveller where the age is set out as a age group
with a interval of 10 years. The age of the traveller under this data is bifurcated in the 7 sevenn
1

section which starts with the age of 1 and there is no maximum age set. The different section of
the age are . 0-15, 16-24, 25-34, 35-44, 45-54, 55-64 and 65 & over.
Sex: category definiens the gender of the person travelling to UK. There 4 section of
gender fro which data is collected that is male, female, the sex is not known and null. The latter
two categorises are least used where it is difficult to identify the gender of a person.
Travel mode: identifies the mode through which the traveller have travelled to UK and
reached other destination in the UK. These category is divided into three sub heading which is
air, water or tunnel (Wickham, 2016). These means travellers under these data have travelled to
UK through these mode only and there is another mode of travelling considered this data.
Visiting purpose: defines the purposed over which a person have gone to UK. There are
different purpose for which people have travelled to UK. These includes , holiday, VFR,
business, study and miscellaneous. For each of a passenger a single purpose is allotted that is no
person have two purpose of travelling UK under this data.
Residential status: This defines the national residential status of the travellers visiting
UK. This is dived into two subheading only that is UK resident and oversea resident. All those
passengers who are coming from different nations are includes under a single category of
oversea resident.
Duration : defines the length of stay in UK and the visit is recorded as per the nights
stayed in UK. This is rerecorded as departure of the overseas resident and the arrival of UK
resident.
Country: this category defines the place of resident of each of the person either a UK
resident or a overseas resident who have visited UK in the third quarter of 2017. There are
almost 65-70 nation which from where travellers have visited UK ad all mentioned in the data.
Package: define the travelling pattern of the visitors to UK as whether they have come
alone or have came with group under a tour package. There are two sub heading in this
categories which is independence and non independent (Agresti, 2018). The former one defines
that the traveller have came independently and the latter one denotes that visitors have come
under a tour package.
Sampling: defines the sizes of the sample for this data the range varies from 1-71.The
sample depicts that number of people from the main IPS used to support each row of
2

information in the Travel pac dataset. This can be used as an indication of the reliability of the
data being examined.
Visit: category defines a complete round trip (Mi and et.al., 2016)). For the UK
resident it is department form UK. For those who have come to visit UK or went abroad on
more than one occasion are counted for each of their visit.
Night: The nights are related with the total number of nights which are spent by a person
on a visit in UK.
Spending: depicts the total expenditure made abroad by the UK resident and for the
people visiting UK the spending are considered which are made in UK during a visit.
The tools and techniques used for decision making of the Travel Pac
The tools and techniques that can be used by the management of the travel Pac for
managing the business and making strategic decision have serves different purposes (Chambers,
2017). The data gathered over the travelling patterns of the traveller in UK assist the
management in decision making process. In this regard the organisation can use different
techniques to aid the strategic decision making which includes:
Market research: is a process where by the information is gathered over a certain
market which includes determining the preference of consumers, the presence of competitions in
the marketplace, current size and state and state of the market (Tyanova and et.al., 2016).
Market research is a strategic planning tool as it gives an insight into the the need ad demand of
the consumers and which assist the business in setting mission, goals and strategies for business.
Feasibility analysis: is a planning tool for the business where the projects and set goals
are assessed with determining there potential with actuality and profitability in the actual
practice. With the feasibility analysis the business and its management can decide over pursuing
a opportunity or not with analysing its feasibility, completion and attainable profits. s
Application of data method over the data
Method used for analysing the data data of Travel pac collected by IPS:
The data gathered by IPS can be analysed by use of different techniques and in the
present report descriptive analysis method is used to evaluate the data of travel Pac over
determining the travelling patter of the visitors ( Schabenberger and Gotway, 2017). Under this
method the the data is analyses in context of sex of the people with calculating mean, median
and mode along with standard deviation and others. The data is analyses over the gender that is
3

male, female and not know factors with regard to all other categories of the data. The benefit of
using descriptive analysis is that it helps the business in tracking the performance with
comparing it with set standard benchmark.
1= male
ukos mode
count
ry
purp
ose
pack
age Age Sex
durat
ion visits nights spend
Mean 1.49 1.25 42.50 2.56 1.13 4.11 1.48 2.45 3984.83 43414.27 2801036.64
Median 1 1 35 2 1 4 1 2 1651.376 15054.45 903716.4235
Mode 1 1 20 1 1 3 1 2 0 0 0
Standard
Deviation 0.50 0.55 24.33 1.60 0.34 1.74 0.61 1.62 10430.55 100738.45 7977667.50
Kurtosis -2.00 3.30 -1.06 -1.28 2.71 -0.83
46.5
7 6.55 189.41 86.82 193.14
Range 1 2 82 8 1 8 9 9
227298.5
84
1928207.9
18
215872953.9
55
Minimum 1 1 10 1 1 1 0 0 0 0 0
Confidenc
e
Level(95.0
%) 0.01 0.01 0.52 0.03 0.01 0.04 0.01 0.03 223.73 2160.78 171116.44
2 = female
ukos mode
count
ry
purp
ose
pack
age Age Sex
durat
ion visits nights spend
Mean 2 1 43 3 1 4 2 2 3928 42659 2539932
Median 2 1 34.5 2 1 4 1 2
1552.000
5 13844.223 825688
Mode 2 1 20 1 1 4 1 2 0 0 0
4

Kurtosis -1.98 0.84 -1.06 -1.25 2.70 -0.82
45.0
7 6.68 205.68 94.56 208.77
Minimum 1 1 10 1 1 1 0 0 0 0 0
Maximum 2 3 92 9 2 9 9 9
227298.5
84
1928207.9
18
215872953.9
55
Confidenc
e
Level(95.0
%) 0.01 0.01 0.50 0.03 0.01 0.04 0.01 0.03 203.93 1971.53 156188.03
9=
don’t
know
ukos mode
countr
y
purpo
se
packa
ge Age Sex
durati
on visits nights spend
sampl
e
Mean
1.4886
25478
9
1.2525
14367
8
42.502
15517
24
2.5553
16092
1.1324
23371
6
4.1091
95402
3
1.4808
42911
9
2.4492
33716
5
3984.8
30142
0019
43414.
26976
45673
28010
36.635
68989
2.8253
11302
7
Standa
rd
Error
0.0054
70013
0.0060
06609
9
0.2661
94243
5
0.0175
36933
6
0.0037
09086
8
0.0190
39028
5
0.0066
99965
0.0177
17514
114.13
31457
523
1102.3
00580
7839
87293.
25521
84201
0.0623
68962
5
Media
n 1 1 35 2 1 4 1 2
1651.3
76
15054.
45
90371
6.4235 1
Mode 1 1 20 1 1 3 1 2 0 0 0 1
Standa
rd
Deviat
ion
0.4999
00531
4
0.5489
39736
2
24.327
29952
63
1.6026
87688
6
0.3389
70765
1
1.7399
63050
1
0.6123
04956
1.6191
90798
10430.
54570
28976
10073
8.4534
12546
79776
67.504
9891
5.6998
54403
5
Sampl 0.2499 0.3013 591.81 2.5686 0.1149 3.0274 0.3749 2.6217 10879 10148 63643 32.488
5

e
Varian
ce
00541
3
34833
9
75022
401
07827
3
01179
6
71415
8
17359
1
78840
4
6283.6
60235
23599
5.9517
17882
0159
34022
16
Kurtos
is
-
1.9984
06711
3
3.3005
42518
7
-
1.0571
72346
9
-
1.2804
70225
9
2.7065
11318
2
-
0.8260
17864
46.570
82022
19
6.5505
66135
2
189.41
07358
684
86.821
58817
16
193.13
90486
046
159.76
87842
972
Skewn
ess
0.0455
18037
3
2.0913
15789
0.5748
57925
0.4187
47968
3
2.1693
00178
5
0.0441
78231
6
3.9877
31605
8
2.1437
21808
4
11.852
55495
66
7.6886
43023
3
11.408
97067
55
10.633
15270
11
Range 1 2 82 8 1 8 9 9
22729
8.584
19282
07.918
21587
2953.9
55 125
Minim
um 1 1 10 1 1 1 0 0 0 0 0 0
Maxi
mum 2 3 92 9 2 9 9 9
22729
8.584
19282
07.918
21587
2953.9
55 125
Sum 12433 10461
35497
8 21342 9458 34320 12368 20456
33281
301.34
59996
36259
5981.0
73666
23394
25798
1.2819 23597
Count 8352 8352 8352 8352 8352 8352 8352 8352 8352 8352 8352 8352
Confid
ence
Level(
95.0%
)
0.0107
22582
5
0.0117
74445
6
0.5218
06758
8
0.0343
76740
7
0.0072
70730
4
0.0373
21219
3
0.0131
33593
6
0.0347
30723
223.72
92815
756
2160.7
82613
9686
17111
6.4372
77524
0.1222
58639
9
null
6

ukos mode
countr
y
purpo
se
packa
ge Age Sex
durati
on visits nights spend
sampl
e
Mean
1.5026
78155
6
1.2730
55426
2
42.287
72706
1
2.5485
56124
8
1.1320
44713
6
4.1191
19701
9
1.4770
61015
4
2.4343
26967
9
3938.1
19664
8812
42614.
41118
30071
27407
45.129
72624
2.8180
01863
1
Standa
rd
Error
0.0053
95641
0.0059
8853
0.2593
14971
3
0.0172
70803
0.0036
53326
2
0.0187
57631
6
0.0065
77507
2
0.0174
80426
1
111.32
77975
252
1074.1
50357
8324
85006.
96434
38363
0.0610
80953
2
Media
n 2 1 34 2 1 4 1 2
1646.7
69
14684.
674
87443
9.307 1
Mode 2 1 20 1 1 3 1 2 0 0 0 1
Standa
rd
Deviat
ion
0.5000
21939
9
0.5549
65833
6
24.031
09769
18
1.6005
10577
7
0.3385
59084
9
1.7382
97160
2
0.6095
47214
9
1.6199
36653
3
10316.
90983
64584
99543.
08482
60957
78777
19.725
9082
5.6604
61272
1
Kurtos
is
-
2.0003
51098
5
2.6626
38417
9
-
0.9939
50976
8
-
1.2721
80527
4
2.7276
11668
4
-
0.8193
73060
5
46.199
57608
5
6.5598
53965
8
192.75
16582
684
88.874
32616
26
197.96
43572
806
160.03
65468
053
Range 1 2 82 8 1 8 9 9
22729
8.584
19282
07.918
21587
2953.9
55 125
Minim
um 1 1 10 1 1 1 0 0 0 0 0 0
Maxi
mum 2 3 92 9 2 9 9 9
22729
8.584
19282
07.918
21587
2953.9
55 125
Sum 12905 10933
36316
7 21887 9722 35375 12685 20906
33820
571.68
19997
36597
2563.2
39665
23537
51917
4.0889 24201
7

Count 8588 8588 8588 8588 8588 8588 8588 8588 8588 8588 8588 8588
Confid
ence
Level(
95.0%
)
0.0105
76752
8
0.0117
38957
7
0.5083
19653
5
0.0338
54923
8
0.0071
61397
2
0.0367
69465
2
0.0128
93494
6
0.0342
65835
4
218.22
92336
518
2105.5
92804
5594
16663
4.0760
90629
0.1197
33345
1
Interpretation of data and presenting results
The data collected by IPS for travel Pac which has been collects under different
categories is analyses using descriptive analysing tool where the main category is taken as the
gender where mean, medium and mode are calculated along with standard deviation. From the
above table it can be stated that the mean value of the man over residential status the values is
determined as 1.49 this states the fact that most of the male passenger for travel pac are UK
resident who go abroad to visit another places (Friese, 2019). On an average male travellers like
to travel to UK by air and they belong to countries either Malta or Mexico. The purpose their
visiting is generally is holidays and they belong to the age group of 35-44 and they like to stay
at their chosen destination for a time between 14-27 days. The average number of visits by
people in the third quarter of 2017 through Travel Pac numbers to 3984.83 and on a average the
nights stated in a destination by different travellers are 43414.27 with a total quarter spending of
2801036 approximately.
The data analysis for the for the female traveller of Travel Pac it can be stated that most
of the women visitors belong to UK as the mean value have been calculated as 2 which means
most of them are UK residents. The travel mode has given a mean value of 1 depicting that the
females loves to travel through air mode only. The mean value of the country has came as 43
which means on an average most female passengers love to travel to Malta. They also prefer to
travel independently and rather going with a tour package. The female passengers also belonged
to the age group of 35-44 and on an average there are 3928 visits in the third quarter of 2017
by female travellers. Also, the total nights stays in this quarters were numbered to 42659 and
there was a total spending of 2539932 in third quarter of 2017 by female passengers.
With this it can be interpreted that the age groups and travelling mode of both male and
females traveller is same and they went for a visit for holiday purpose. The spending by male
8

traveller is more than female travellers. The fact is depicted that male traveller are UK residents
and the average number of female travellers are overseas residents. Also the visits paid by both a
male and females are similar with not a much difference in the visits. The s duration of stay is
more for the male passengers and this can be confirmed with the fact that the nights stay by men
in UK is more as compared to stay by the female travellers.
Presenting recommendation to Travel Pac over a potential target segment
From the above interpretation of the data the recommendations are made to Travel Pac
over the target segment of male and female travellers belonging to the age group of 35-44. This
is suggested that the company must develop such services which can be availed by independent
traveller by including air travelling. Also they are spending good amount so services must be
provided as per their needs and demand and to some extent impersonalise requirement.
CONCLUSION
From the above report it can concluded that data analysis is carried on with descriptive
analysis method by selecting the sex category as key one among the 14 diffident categorises. The
data has been interpreted that both male and female like to send money on holiday trips with
travelling by air and they belong to the age between 35-44. Also the tools and techniques for
decision making have been identified as market research and feasibility analysis.
9

REFERENCES
Agresti, A., 2018. An introduction to categorical data analysis. Wiley.
Chambers, J. M., 2017. Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC.
Friese, S., 2019. Qualitative data analysis with ATLAS. ti. SAGE Publications Limited.
Mi, H and et.al., 2016. PANTHER version 11: expanded annotation data from Gene Ontology
and Reactome pathways, and data analysis tool enhancements. Nucleic acids research.
45(D1). pp.D183-D189.
Schabenberger, O. and Gotway, C. A., 2017. Statistical methods for spatial data analysis.
Chapman and Hall/CRC.
Silverman, B. W., 2018. Density estimation for statistics and data analysis. Routledge.
Tyanova, S. and et.al., 2016. The Perseus computational platform for comprehensive analysis of
(prote) omics data. Nature methods. 13(9). p.731.
Wickham, H., 2016. ggplot2: elegant graphics for data analysis. Springer.
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