Page 182 - Contributed Paper Session (CPS) - Volume 8

CPS2231 Syafawati A. R. et al.
furnishings and household equipment; health; transport; communication;
recreation services and culture; education; restaurants and hotels; and
miscellaneous goods and services. To understand further the impact of
tourism industry on CPI, a simple linear regression analysis is applied to
determine the relationship between tourist arrival and 9 selected main groups
of CPI. Based on Table 3, all main groups has a significant relationship with
tourist arrival except transport. However, the level of correlation between all 8
main groups and tourist arrival is very weak where the value of unstandardized
beta is less than 0.01.

Table 3: Simple Linear Regression Test Model Summary, Tourist Arrival and 9 Main Groups of
CPI

Unstandardized Coefficients Standardized
Main Group t Sig
Coefficients
Food and
nonalcoholic 1.546E-5 0.000 0.453 3.866 0.000
beverages
Alcoholic beverages
and tobacco 5.211E-5 0.000 0.466 4.014 0.000
Clothing and
footwear -6.325E-6 0.000 -0.523 -4.675 0.000
Health 8.861E-6 0.000 0.458 3.925 0.000

Transport 2.731E-6 0.000 0.120 0.924 0.360
Communication 4.508E-6 0.000 0.423 3.552 0.001

Recreation services
and culture 5.250E-6 0.000 0.430 3.632 0.001
Restaurants and
hotel 9.806E-6 0.000 0.450 3.836 0.000
Miscellaneous
goods and services 8.367E-6 0.000 0.422 4.544 0.001

A stepwise regression is used to determine the main group that is most
affected by the tourism industry. Based on Table 3, only 8 main groups has a
significant relationship with tourist arrival thus were selected for stepwise
regression analysis. Table 4 shows that the group Clothing and Footwear is
statistically significant and moderately correlated with p-value = 0.000 and
variance inflation factor (VIF) = 1.0. The other main groups were automatically
removed by the stepwise regression due to multicollinearity which will
increases the standard errors of the variables.

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