Page 181 - Contributed Paper Session (CPS) - Volume 8

CPS2231 Syafawati A. R. et al.
When the correlation coefficient between y and x is computed by first
eliminating the effect of all other variables, it is called partial
correlation coefficient. It is computed as follow:

1 − 212

1.

The statistical test and data analysis were done through SPSS and
Microsoft Excel.

3. Result
The relationship between tourism industry and CPI is obtained by applying
Simple Linear Regression to monthly data of Melaka’s tourist arrival and CPI
for the year 2013 – 2017. Table 1 shows that there is a low degree of
correlation between the two variables (R = 0.48). Furthermore, only 23 per cent
(R2 = 0.230) of the variation in CPI can explained a linear relationship with the
number of tourist arrival as indicated in Table 1.

Table 1: Simple Linear Regression Test Model Summary, Tourist Arrival and CPI

Model R R2 Adjusted R2 Standard Error of the
Estimate
1 0.480 0.230 0.217 3.8957
a

Table 2 shows the coefficient table and indicates the value of beta
(standardized and unstandardized) for the two variables. Results indicate that
there is a significant relationship between tourist arrival and CPI where the p-
value = 0.000 which is less than α = 0.05. From Table 2, it further proved that
the relationship between tourist arrival and CPI is at a low degree of correlation
where β = 1.014E-5

a
Table 2: Coefficients of Simple Linear Regression Model, Tourist Arrival and CPI
Unstandardized Coefficien ts Standardized
Model t Sig
B Std. Error Coefficients
3.211
1 Constant 99.486 30.988 0.000
Tourist 1.014E-5 0.000 0.480 4.165 0.000
Note: ‘a’ denote dependent variable: CPI

CPI is used to measures the weighted average of prices of a basket of
consumer goods and services. Those goods and services are broken into 12
main groups: food and non-alcoholic beverages; alcoholic beverages and
tobacco; clothing and footwear; housing, water, electricity, gas and other fuels;

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