TAYLOR
Foundation In Business
Integrated Project
BUSINESS SCHOOL
1. Independent: Arrival
Dependent : Receipts (RM)
3. Yes, because when the arrivals (million) increase, the receipts (billion) increase. This shows a positive linear regression between the arrivals (million) and the receipts (billion).
4. SUMMARY OUTPUT
5. a) regression line
Y= a + bX
a = 22.3161 b = 3.2833
Y = 22.3161 + 3.2833X
b) Y- intercept : When there is no arrivals, the receipt will be 22.3161 billion
Slope : Every unit increase in arrivals (million), the receipts (billion) will increase in 3.2833unit.
6. Yes, 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. Also, the determination of correlation also shows that 97.95% of the variation in the arrivals (million) of tourism,Y is explained by the variation of the receipts (billion). Only remain 2.05% is explained by other factor.
7. I would recommend using this regression equation to predict the dependent variable. This is because the coefficient of determination, r2is 97.95% which is considered a good prediction. The standard error is 1.78 which is quite high. It may be because of the variable X and Y consist of decimal places. Also, the scatter diagram shows it has a strong positive correlation between the variable because all the point is close fit to the line. Therefore, the regression equation Y = 22.3161 + 3.2833X is a good predictor for the dependent variable, receipt (billion).
2.
Part B
1.
Centre Location
Mean: The average arrivals (million) of tourism to Malaysia are 22.2978.
Median: Half of the arrivals (million) are below 23.65 and half are above it.
Dispersion
Standard deviation: Spread 3.3682 around the mean.
Central Location
Mean: The average receipts (billion) from arrivals of tourism to Malaysia is 50.8933
Median: Half of the receipts (billion) are below 53.4 and half are above it.
Dispersion
Standard deviation: Spread 11.1739 around the mean.
2.
Independent Variable
Number of classes
2k>n, n=9
24 = 16 ( > n=9)
Therefore, k=4
Class interval or width
Frequency Data
Dependent Variable
Number of classes
2k>n, n=9
24 = 16 ( > n=9)
Therefore, k=4
Class Interval and Width
Frequency Data
3.
4. The arrivals of tourist histogram show that 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 frequency.