Contents 1 Introduction 3 2 Theoretical basis and research methods 4 2.1 Theoretical basis and Analysis framework 4 2.1.1 Methods of analysis of variance (ANOVA) 4 2.1.2 Literature review 5 2.2 Research methodology 7 3 Results and discussion 7 3.1 Results 7 3.2 Discussion 9 4 Conclusion 10

Figure 31: The average number of foreign visitors to Ho Chi Minh City per month (2005 - 2013). (Source: Table 3-1). Unit: tourists. 9

Table 31: Foreign Tourists having arrived in Ho Chi Minh City (2005-2013) (Source: Ho Chi Minh City Department of Culture, Sports and Tourism; Unit: Tourists) 7 Table 32: the output of the ANOVA analysis – The Summary table (Source: Excel) 8 Table 33: the output of the ANOVA analysis – the ANOVA table (Source: Excel) 8

Abstract
Seasonality is “a temporal imbalance in the phenomenon of tourism” (Butler, 1994). The topic of tourism seasonality has been analyzed over decades, by many touristic enterprises and policymakers. It is useful to have an understanding of the seasonality phenomenon in Vietnamese tourism. The former will represent the seasonal concentration of the number for foreign visitors coming to Ho Chi Minh City. Besides, the results conducted, the latter investigates the causes of seasonal tourism in Ho Chi Minh City. The decomposition technique used in this study is primarily ANOVA statistical model for a single factor based on the secondary data of monthly tourist arrivals in the City during the period from 2005 to 2013. It has pointed out that peak season of Ho Chi Minh City, from late November to early April, proving the higher demand of foreign tourists than off-peak season.

Key words: Tourism, Seasonality, Time, Tourists, Peak Season, ANOVA analysis.

Introduction
In the recent years, tourism has played an important role in industrialization period of time. In Vietnam, “tourism accounts for approximately 4% of GDP” (Từ Lương, 2010). Besides, tourism is considered as the promotion of country image, culture and native people to everyone all over the world. In 2013, it is estimated 7,5 million of people visit Vietnam which demonstrated that there was a 10,6% increase in the amount of the tourists in comparison with the same period last year. And the percentage of leisure and holiday tourists was 65% that created the revenue about 200 000 billion VND.
Seasonal impact is one of the most significant parts we should mind when researching the plan for the tourism development. It is defined as the cyclical movement under the demand and supply in the tourism market which depends on several factors such as weather, climate, tourism purposes and different types of tourism. And we can choose time as the representative concept of all the factors as time influences directly on them.
The question arises is how time has effects on the amount of foreign tourist arrive in Vietnam during a year? The aim of the article is using ANOVA analysis to answer the question mentioned above. The methodology is examined in greater depth, reviewing some of the Theoretical basis and former Analysis framework and suggesting additional modifications to the model framework in order to bring the ANOVA model closer to reality.

Theoretical basis and research methods
Theoretical basis and Analysis framework
Methods of analysis of variance (ANOVA)
Method of analysis of variance (ANOVA) was born in 19th century by English mathematician Ronald Aylmer Fisher. This is a statistical technique used when we want to compare the average of two or more populations based on some samples of these populations. Variance characterizes the relative degree of dispersion of the observations compared to the average number so the comparison is easier. In the study of the impact of on the amount of foreign tourists to Ho Chi Minh City, we will use the one-way (single factor) ANOVA analysis, which evaluate the concentration of sample.
The assumptions are used in the single factor analysis of variance: * The sample collected from the populations must be normally allocation or approximately normally allocation. * The variance of the populations must be equal. * The sample must be random and independent.
Suppose that there are k groups: n1, n2… nk (may vary in size) µ1, µ2... µk is called the average of the groups and Xij is the jth observation of group i
Hypothesis: H0: μ1=μ2=…μk H0: Not all μi are the same

Average of each group: x=j=1nxijni
Average between groups: x=i=1nj=1nxijni
Differences of squares:
Squared differences of internal group SSW (within groups’ sum of square):
SSW = SS1 + SS2 +… + SSk and SS= j=1n1(x1j-x1)2
Squared differences between the groups SSG (between groups sum of square):
SSG= i=1kni(xi-x)2
The sum of squared differences SST (total sum of square): SST = SSW + SSG
Variance internal group MSW (within groups’ mean square): MSW=SSWn-k
The variance between groups MSG (between groups mean square): MSG=SSGk-1
One-way ANOVA table: Source of Variation | SS | df | MS | F ratio | Between Sample | SSG | k-1 | MSG=SSGk-1 | F=MSBMSW | Within Sample | SSW | n-k | MSW=SSWn-k | | Total | SST | n-1 | | |

Where: k = number of populations n = sum of the sample sizes from all populations df = degrees of freedom
Rule: Reject H0 at α if F>Fk-1,n-k,α
Where: α = significance level, or Type I error rate
Conclusion: There is evidence that at least one μi is different from the others.

Literature review
The study of R. W. Butler, "Seasonality in Tourism: Issues and implications" in 2001, has confirmed that seasonality has a dialectical relationship with the number of tourists in tourism industry and seasonality is the determinant of global tourism industry. The research paper has taken the advantages of identifying the seasonality to the development of the tourism industry. For example, determining the season helps exploit the natural resources in a reasonable way, building of infrastructure meet the demand of tourists during the peak times, recruiting and training of professional human resources and investment used and allocated reasonably is very necessary. The study emphasizes the “seasonality in tourism” through a basic analytical method, presenting the author's own view concerning causes, forms, perceptions and impacts of this issue.
Two authors, G.Kalai Selvi - Assistant Professor of Commerce, ST Hindu College and Isabella S. Jesi - Associate Professor of Commerce, Holy Cross College has also exploited the seasonality of tourism in their "Analysis of the Seasonality of Tourism Market in Thanjavur District". Their research has clarified the seasonal variation in the number of domestic and foreign tourists to Thanjavur district – Taminadu state. It was characterized by calculating and comparing the seasonal coefficients. The analysis also is objective and partly still unconvincing because India is a country of many barriers for attracting tourists. However, the authors have made some predictions and strategies for tourism development here.
In the article "Seasonality in tourism demand" on Eurostat, the report of the European Commission, the travel demand model seasonally of European Union (EU) people was analyzed statistically. From the data of the period from 2004 to 2009, all statistical report divided a year into four quarters and make comparison the results of travel services under a variety of factors such purposes, vacation time and the time of year.
The above study shows that the time factor has an impact on the amount of tourists. However, different countries, regions have different seasonality due to the distinctions of climate, weather, culture and development policies of the Government. Ho Chi Minh emerges as a tourist town not because of having multiple resort locations as other cities of Vietnam but the dynamism of the mode of life, the diverse culture, the comfort of the services provided and the variety in types of entertainment. To analyze more clearly about the seasonality of tourism services in Ho Chi Minh City, we did a research about the impact of seasonal tourism on the number of visitors to Ho Chi Minh City.

Research methodology
The data in this study is a secondary data, being collected indirectly from the Ho Chi Minh City Department of Culture, Sports and Tourism. The statistical data, all foreign visitors to Ho Chi Minh City in the period from 2005 to 2013 including all the months of a year.
This study also used the methods of analysis, synthesis, and statistical comparisons. Amongst, the quantitative model used to research is the one-way analysis of variance (ANOVA).

Results and discussion
Results
The research was conducted basing on the statistic data Ho Chi Minh City Department of Culture, Sports and Tourism about the amount of foreign tourists having arrived in Vietnam every month during the 2005-2013 period of time.
This is the result illustrating the information collected: Year | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | January | 202 579 | 278 140 | 296 896 | 163 656 | 123 580 | 248 400 | 163 587 | 180 241 | 350 407 | February | 228 460 | 225 173 | 343 414 | 152 550 | 150 741 | 258 811 | 169 580 | 143 044 | 374 079 | March | 239 269 | 281 014 | 357 178 | 164 618 | 202 882 | 355 496 | 213 320 | 257 638 | 414 758 | April | 266 800 | 333 042 | 184 361 | 206 280 | 241 362 | 175 076 | 190 100 | 299 978 | 186 459 | May | 199 800 | 368 198 | 166 860 | 118 135 | 185 412 | 135 600 | 138 760 | 247 611 | 155 298 | June | 224 774 | 308 057 | 189 474 | 149 530 | 266 337 | 172 240 | 187 070 | 319 495 | 171 160 | July | 273 469 | 139 172 | 221 940 | 219 678 | 114 397 | 163 060 | 134 774 | 146 900 | 183 037 | August | 320 200 | 145 824 | 144 932 | 186 644 | 103 620 | 130 054 | 208 400 | 125 012 | 163 256 | September | 341 978 | 175 661 | 213 608 | 241 726 | 125 220 | 191 085 | 292 007 | 143 600 | 202 768 | October | 133 304 | 209 475 | 228 400 | 106 942 | 118 200 | 224 600 | 116 156 | 111 148 | 280 440 | November | 221 440 | 203 884 | 374 800 | 147 998 | 150 119 | 303 658 | 169 040 | 229 450 | 351 923 | December | 245 200 | 295 695 | 371 516 | 180 900 | 221 400 | 385 428 | 199 691 | 251 279 | 410 068 |
Table [ 3 ] [ 1 ]: Foreign Tourists having arrived in Ho Chi Minh City (2005-2013) (Source: Ho Chi Minh City Department of Culture, Sports and Tourism; Unit: Tourists)
According to the table, we have: N= 108, = 5%
From the hypothesis test:
H0: μ1 = μ2 = μ3: the average amounts of foreign tourists every month are the same
H1: The average amounts of foreign tourists every month are different
Taking the ANOVA Single Factor to find out the relationship between the numbers: SUMMARY | | | | | Groups | Count | Sum | Average | Variance | Column 1 | 9 | 2007486 | 223054 | 5570002056 | Column 2 | 9 | 2045852 | 227316.8889 | 7238107630 | Column 3 | 9 | 2486173 | 276241.4444 | 6944397932 | Column 4 | 9 | 2083458 | 231495.3333 | 3265977034 | Column 5 | 9 | 1715674 | 190630.4444 | 5959528920 | Column 6 | 9 | 1988137 | 220904.1111 | 3933055980 | Column 7 | 9 | 1596427 | 177380.7778 | 2676985695 | Column 8 | 9 | 1527942 | 169771.3333 | 4199428092 | Column 9 | 9 | 1927653 | 214183.6667 | 4773903005 | Column 10 | 9 | 1528665 | 169851.6667 | 4314459153 | Column 11 | 9 | 2152312 | 239145.7778 | 7260249619 | Column 12 | 9 | 2561177 | 284575.2222 | 7275763311 |
Table [ 3 ] [ 2 ]: the output of the ANOVA analysis – The Summary table (Source: Excel) ANOVA | | | | | | | Source of Variation | SS | df | MS | F | P-value | F crit | Between Groups | 1.41E+11 | 11 | 12785012879 | 2.419423722 | 0.010579 | 1.8898 | Within Groups | 5.07E+11 | 96 | 5284321535 | | | | | | | | | | | Total | 6.48E+11 | 107 | | | | |
Table [ 3 ] [ 3 ]: the output of the ANOVA analysis – the ANOVA table (Source: Excel)
Reject H0 since F > F crit (2.419423722 > 1.8898)
Therefore, we conclude that the average amounts of foreign tourists to Ho Chi Minh City every month are different.
Discussion

Figure [ 3 ] [ 1 ]: The average number of foreign visitors to Ho Chi Minh City per month (2005 - 2013). (Source: Table 3-1). Unit: tourists.
The diagram shows that foreign tourists visited Ho Chi Minh City increasingly from November to April especially in March. This interval is called the peak season of international tourism, accounting for nearly 60% of foreign visitors to Vietnam in the year. This result can be explained by both factors: spare time and type of travel.
First, free time is a precondition of traveling time when they can travel without worries about their work and lots of personal problems. In spite of the fact that foreign visitors can have an international trip in National Holiday and Vacation such as summer time, spring-winter time, Christmas, or anytime they can spend time adventuring the landscapes, experiencing the culture…; the peak season is November to April. On the contrary, the peak domestic travel is summer (falling June to September) and early spring right Lunar New Year holidays (around January to March). It is explained by the characteristic holidays in Europe and American. Apart from summer vacation, they have winter vacation lasting 3-4 weeks which the demand of traveling to cooler or warmer places accelerates. Vietnam will be an ideal option due to the benign climate during the year with beautiful beaches and unique culture.
Second, types of tourism also have big effects on the selection of the tourists. Vietnam is famous for distinct culture which maintaining the traditions in the past particularly in Lunar New Year and many kinds of food and festivals. Besides, the tourists are able to book tickets from Ho Chi Minh City – one of two places in which international airport locate, to pay more visits to other interesting places in Vietnam such as Ha Long Bay, coastal city of Nha Trang, Hue Ancient,…Even Vietnamese people have never missed the Flowers Festivals in Da Lat City, Gong Central Highland Festival or Buffalo Sacrifice in Dak Lak Province… Therefore, foreign tourists are ready to begin a nice trip to Vietnam in February and March to enjoy the space of Traditional New Year of Asia. Moreover, their trip can last longer until the end of March that makes it easy to predict the trend of the amount of foreign visitors. This is not only helpful for small local business but also for the government in planning the suitable and effective strategies to develop the tourism industry.
As the statistics and analysis above, we can conclude that the study results are similar to previous studies on seasonal tourism numbers. Simultaneously, we certainly understand the relationship of the ANOVA model in the theory and practice.
Conclusion
It is crucial to pursue the suitable policies based on the seasonality to develop the tourism industry. Ho Chi Minh city is famous for variety of events such as The Tet Flower Festival, fireworks displays on New Year’s Eve and others festival. These festivals need to be improved about both the spread and the depth. However, in the peak-season, airport companies should facilitate the tourists to do the entrance proceedings, enhance the quality of services and the security. The systems of hotels and restaurants should be combined coherently with tourism companies to help the tourists have a nice trip.
There is no doubt that the most fundamental factor of tourism is the seasonality which is presented in the differences of the amount of visitors to Ho Chi Minh City every month. With the one-way ANOVA analysis, the study showed that the peak seasons are from November to April of the next year. Many kinds of wonderful activities for all the tourists are ready to welcome all the tourists from everywhere on the world in beautiful weather. Furthermore, this is suitable time for the tourists especially from American and Europe travelling to other places. Catching the problem, the administration should speed up the boosting policies, promotion in the peak seasons. Simultaneously, many tourism companies need to develop, diversify many types of tour so that the tourist could be full of enjoyment and experiences.

References:
Books:
Trần Văn Hoàng, Lê Hồng Vân (2013), "Statistics for Business", Vietnam National University - Ho Chi Minh City Publisher, Ho Chi Minh City.

Journal, Newspaper and Newsletter Articles:
G. Kalai Selvi, S. Jesi Isabella (2012), "Analysis of the Seasonality of Tourism Market in Thanjavur District", IJEMR, Vol 2 (Issue 12), 1-10.
Joan C. Henderson (2000), "War as a tourist attraction: the case of Vietnam", International Journal of Tourism Research, Volume 2 (Issue 4), 269-280.
R. W. Butler (2001), "Seasonality in Tourism: Issues and Implications", Seasonality in Tourism, Chapter 2, 5-19.

Electronic Resources:
European Commission eurostat (2010), "Seasonality in tourism demand", http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Seasonality_in_tourism_demand#Further_Eurostat_information, 10/04/2014.
Từ Lương (2010), "Ngành du lịch đến năm 2020 đóng góp 8% GDP", http://baodientu.chinhphu.vn/Tin-noi-bat/Nganh-du-lich-den-nam-2020-dong-gop-8-GDP/33282.vgp, 09/04/2014.
Vietnam National Administration of Tourism (2014), "Tổng thu từ khách du lịch giai đoạn 2000 – 2013", http://vietnamtourism.gov.vn/index.php/items/13462, 09/04/2014.
Wikipedia (2014), "Analysis of variance", http://en.wikipedia.org/wiki/Analysis_of_variance, 08/04/2014.
Xinhuanet (2013), "Hơn 7,5 triệu lượt khách du lịch quốc tế đến Việt Nam năm 2013", http://www.baomoi.com/Hon-75-trieu-luot-khach-du-lich-quoc-te-den-Viet-Nam-nam-2013/137/12745930.epi, 08/04/2014.

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[ 1 ]. “A seasonal coefficient is a measure of how a particular season compares with the average season which are calculated as percentage ratio between the average level of each month registered on a period of a few years and the general monthly average” (Selvi and Isabella, 2012).