Estimate the parameters of a multiple linear regression model for visitor arrivals using an… 1 answer below »

Questions
“Question Zero” – referring to your last case study report.
Refer to your data that you used for Question #5 in Case Study Report #1.
Provide a line chart of the relevant time series for the last 4 years.
Include a relevant heading for your line chart.
1. Estimate the parameters of a multiple linear regression model for visitor arrivals using an
intercept, a time variable and a suitable set of dummy variables for the month effect. Explain
what you have done, provide relevant screenshots, and write out the overall regression
equation.
2. Explain the meaning of the intercept, the coefficient of the time variable, as well as the
coefficient for the June dummy variable. (If June is your baseline, then change June to
September for this question).
3. What is the R-squared for your regression? Explain what this represents, and comment on the
importance (or not) of this statistic.
4. Write out the regression equation for each of the following months: March, June, September,
and December. Draw a graph that represents the four different regression lines for each of the
following months: March, June, September, and December.
5. Conduct a test for the overall significance of the model. Explain and analyse your results.
6. Conduct a test for the individual significance of the coefficient for the time variable. Explain
and analyse your results.
Conduct a test for the individual significance of the coefficient for the December dummy
variable. (If December is your baseline, then change December to March for this question).
Explain and analyse your results.
6
7. Use your regression equation to forecast tourist arrivals for each month beyond your sample
period. Show your working for only the December forecast as an example. Plot your original
data against time and include your forecasts in this plot. Clearly differentiate between the
original data and the forecasts in your plot.
8. Based on your analyses above, are there any modifications that you would suggest to arrive at
an improved regression model?
Compare and contrast the performance of your model from Case Study Report #1 and your
regression model above. Based on your analysis, which model would you utilise for your
specific business case?
9. Research question (refer to the material on iLearn): In the context of judgmental forecasting,
what are subjective assessment methods? Critically evaluate how subjective assessment
methods may assist in addressing your business problem.
10. Research question (refer to the material on iLearn): In the context of judgmental forecasting,
what is scenario analysis? Critically evaluate how scenario analysis may assist in addressing
your business problem.