TAYLOR

Quantitative Techniques

E-Portfolio

LEOW ZHI WEY

 

             During this integrated assignment, my group member and I had chosen Tourist arrivals & receipts to Malaysia as an appropriate secondary data in the appendix.  The independent variable is Arrivals and Receipts (RM) as dependent variable. By using these variables, we made a scatter plot diagram with Microsoft Excel. It is appropriate to fit a regression line in this case, because when the arrivals (million) increase, the receipts (billion) increase. This shows a positive linear regression between the arrivals (million) and the receipts (billion). Furthermore, we had formed a regression line (Y = 22.3161 + 3.2833X) by using the formula Y=a+bX .  It explained for the y-intercept, when there are no arrivals to Malaysia; the receipt will be 22.3161billion.  The interpretation for the slope would be every unit increases in arrivals (million), the receipts (billion) will increase in 3.2833unit. 

           

             Based on the analysis, we think that the dependent variable is explained by the independent variable because the coefficient of correlation, r 0.9897 which shows a very strong relation between them. We recommend using this regression equation to predict the dependent variable because the coefficient of determination, r2is 97.95% which is considered a good prediction.  Besides, the scatter diagram shows it has a strong positive correlation between the variable because all the point is close fit to the line. Accordingly, the regression equation (Y = 22.3161 + 3.2833X) is a good predictor for the dependent variable - receipt (billion).

 

            Moreover, we had constructed two descriptive tables using the similar data to the previous one. We choose mean and median as the central location and standard deviation as dispersion value to interpret.   For the independent variable as known as arrivals, the average arrivals (million) of tourism to Malaysia are 22.2978. The median shows half of the arrivals (million) are below 23.65 and half are above it. The standard deviation shows that it spread 3.3682 around the mean. For the dependent variable as known as receipts, the average receipts (billion) from arrivals of tourism to Malaysia are 50.8933.  The median shows half of the receipts (billion) are below 53.4 and half are above it. The standard deviation shows that it spread 11.1739 around the mean.

 

            Furthermore, we had constructed two suitable frequency tables for our data set for each independent variable and dependent variable.  By using the formula 2k>n, we found the number of classes; by using i(H-K)L/k, we found the class interval.  Hence, H is the highest value and L is the lowest value in each variable.  Then, we combined and constructed two graphs (histogram).We had also done elaboration of our answers through the graph.  It shows that the arrivals of tourist histogram has highest frequency of 5 arrivals to Malaysia is between 23.42 – 25.74 and 2 frequency lie in 16.43 – 18.75. There is 1 frequency in 18.76 – 21.08 and 21.09 – 23.41 respectively.  For the receipts from tourism histogram, the highest frequency is 3 which lie in 48.74 – 57.10 and 57.11 – 65.47 respectively. The lowest frequency 1 lies in 40.37 – 48.73. The 32.00 – 40.36 has 2 frequencies.    Through the graph, we can see the highest and lowest frequency easily.