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    Time series analysis and seasonality

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    U.S. Virgin Islands is a popular tourist destination, particularly during winter months. Tourists come from all over the world. Majority of these tourists typically stay 3- 5 nights in hotels, and spend significant amount of money on a variety of activities including playing golf, scuba diving, snorkeling, and just enjoying the beach among other things. Travel agents, primarily from the U.S., and Europe offer package deals to the tourists. One such travel agent, Calypso Travels is contemplating to offer a 5 night special that includes hotel stay at three-star hotels (they also offer room views on the web), 2 Scuba lessons, one day all expense paid golf, one free massage, and all you can eat buffet every night. This package would only cost $580 per person with double occupancy. Before Calypso can finalize this offering they must verify that they will have a guaranteed customer base for 1200 for next year. Calypso engaged a market research firm to investigate into the nature of the tourism market in U.S. Virgin Islands. After a 3 month investigation the market research firm gave a report to Calypso. In its report the market research firm predicted that U.S. Virgin Islands will experience about 7-10% growth in attracting tourists for the next 4-5 years. It also predicted that travel agents such as Calypso will have about 25-40% of the market share, and there are about 15 major travel agents worldwide that compete for this market share. On an average between October and January U.S. Virgin Islands hosts approximately 75,000-77,000 visitors for a period of 4-6 nights per month.
    Following is the monthly data on tourists staying at U.S. Virgin Islands for the past several years. Using a Time-series analysis arrive at a conclusion either similar to that of the market research firm, or contradict their recommendation. Please show how you came to this conclusion.
    Month Number of Tourists
    (Staying 4-6 days)
    Jan’ 99 106,000
    Feb’ 99 98,000
    Mar’ 99 94,000
    Apr’ 99 43,200
    May’99 38,000
    June’99 29,000
    July’ 99 11,400
    Aug’ 99 16,000
    Sept’ 99 12,600
    Oct’ 99 54,000
    Nov’ 99 68,900
    Dec’ 99 97,650
    Jan’ 2000 105,000
    Feb’ 2000 101,000
    Mar’ 2000 91,000
    Apr’ 2000 71,400
    May’ 2000 38,900
    Jun’ 2000 28,000
    Jul’ 2000 31,000
    Aug’ 2000 9,500
    Sept’ 2000 5,400
    Oct’ 2000 67,000
    Nov’ 2000 89,400
    Dec’ 2000 82,850
    Jan’ 2001 102,400
    Feb’ 2001 95,000
    Mar’ 2001 99,000
    Apr’ 2001 73,000
    May’ 2001 48,000
    Jun’ 2001 28,900
    Jul’ 2001 18,300
    Aug’ 2001 14,200
    Sept’ 2001 10,250
    Oct’ 2001 21,200
    Nov’ 2001 27,600
    Dec’ 2001 42,000
    Jan’ 2002 44,000
    Feb’ 2002 32,400
    Mar’ 2002 31,800
    Apr’ 2002 20,000
    May’ 2002 31,000
    Jun’ 2002 10,700
    Jul’ 2002 9,400
    Aug’ 2002 7,200
    Sep’ 2002 6,700
    Oct’ 2002 51,000
    Nov’ 2002 65,600
    Dec’ 2002 87,000
    Jan’ 2003 89,400
    Feb’ 2003 91,300
    Mar’ 2003 72,000
    Apr’ 2003 41,500
    May’ 2003 29,000
    Jun’ 2003 7,890
    Jul’ 2003 8,800
    Aug’ 2003 9,430
    Sept’ 2003 22,000
    Oct’ 2003 65,800
    Nov’ 2003 87,000
    Dec’ 2003 112,000
    Jan’ 2004 89,500
    Feb’2004 90,200
    Mar’ 2004 65,200

    © BrainMass Inc. brainmass.com March 4, 2021, 6:07 pm ad1c9bdddf
    https://brainmass.com/statistics/regression-analysis/time-series-analysis-and-seasonality-30274

    Solution Preview

    Hi,
    See the attached excel file for detail calculations. The results of time series analysis suggest that recommendations of market research firm need to be scrutinized. I have followed following steps

    1. Calculate seasonal indices for months
    2. From seasonal indices get the deseasonalized per month number of ...

    Solution Summary

    Excel file contains time-series analysis to confirm or reject the firm's recommendation.

    $2.49

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