The response to the assignment must be provided in the form of a  professional report with no more than 8 pages (including cover page).  The structure of your professional report must include: 1] A Title, 2]  An Executive Summary, 3] An Introduction, 4] Analysis &  Interpretation, and 5] Conclusions.
You must submit an electronic copy of your assignment in Canvas. See the attached Template of your submission for more details.

This  assignment requires the use of Microsoft Excel. If you have Windows,  you will need to use the Data Analysis Tool Pack. If you have a Mac with  Excel 2011, you may need to use StatPlus:MAC LE. You will need to  include your Excel output as an excel file submitted with your report.  The excel file needs to be clear and carefully organised and must show  relevant workings underlying the Professional report and associated  statistical analysis. It will be treated as an appendix to your report,  i.e. not included in the page count. Do not refer to the excel workbook  within the Professional report. You will need to take the key results  from your workbook and incorporate into your report.
Presentation Instructions:
Your written professional report should comply with the following presentation standards:
1. Typed using a standard professional font type (e.g. Times Roman), 12-point font size.
2. 1.5-line spacing, numbered pages, and clear use of titles and section headings.
3. Delivered as a Word (.doc or .docx) or PDF (.pdf) file.
4. Checked for spelling, typographical and grammatical errors. Where relevant, round to 3 decimal places.
5. With all relevant tables and charts, the report should be no more than 8 pages long.
Problem Description:
The  commuting time in cities around the world has been rising. Taking  Australia as an example, workers spent an average of almost 66 minutes  travelling to and from work each day in 2017. The long-duration of  commuting has implications for workers’ labour force participation and  productivity. More importantly, the impact of the long commuting is  likely to go beyond the work and productivity itself, as it might also  affect workers’ psychological health.
You are appointed by the  Department of Health in Australia to study the impact of commuting time  (and other potential factors) on workers’ psychological health. The data  are drawn from the 2017 HILDA (Household, Income and Labour Dynamics in  Australia) survey. You may assume the department members have a good  understanding of basic statistics.
Useful article readings:
Each question is described below (3 + 4.5 + 8 + 7.5 + 2 + 5 = 30 marks; professional report = 10 marks):
Locate the data file (IndividualBusStats.xls) on CANVAS.
1.  Use appropriate graphs to interpret the relationship between (i)  psychological health and commuting time; (ii) psychological health and  wage; and (iii) psychological health and age. Carefully interpret and  explain. [Topic 1, 9]
2. Calculate the sample correlation and  covariance for the above 3 relationships in question 1 using Data  Analysis Tool Pack. In addition, for the relationship between  psychological health and commuting time, you are required to calculate  the sample correlation and covariance using a second method (using basic  Excel formulae without Data Analysis Tool Pack). The calculations by  the second method should be carefully laid out in Excel and should NOT  use any hard-wired Excel statistical functions e.g. COVARIANCE.S,  CORREL, et al. You can use the Excel sort command, the sum command, and  any other non-statistical excel commands). Carefully interpret your  results. [Topic 9]
3. Use simple regression to explore the  relationship between (i) psychological health (Y) and commuting time  (X); (ii) psychological health (Y) and wage (X), respectively. You may  use Data Analysis Tool Pack for this. Based on the excel regression  output, first write down the estimated regression equations, then carry  out any relevant two-tailed hypothesis tests using the critical value  approach at the 5% significance level. Carefully interpret your  hypothesis test results. [Topic 9-10]
4. Now use multiple regression  to explore the relationship of psychological health (Y) with, commuting  time (X1), age (X2) and wage (X3). You may use Data Analysis Tool Pack  for this. Based on the excel regression output, first write down the  estimated regression equation, then carry out any relevant two-tailed  hypothesis tests using the critical value approach at the 5%  significance level, and an overall significance test using the p-value  approach. Carefully interpret your hypothesis test results. [Topic 11]
5.  Using your multiple regression results to predict psychological health  for a typical worker with commuting time equal to: (i) 0.5 hour; (ii)  2.0 hour, respectively. Here we assume that age (X2) and wage (X3) take  their sample mean values. (Hint: this means you will have 2 distinct  predictions for psychological health.) Carefully interpret your results.  [Topic 10-11]
6. Workers’ psychological health may be impacted by  other factors too. If you could request additional data to study the  determinants of workers’ psychological health, what extra variables  would you request? Illustrate two such variables. Carefully explain why  you choose these two variables (by drawing evidence from the literature  such as journal articles, newspapers, et al), types of your proposed  variables (e.g. numerical or categorical), and how each of your proposed  variables will be measured in the regression model. [Topic 1, 10-11]