This is an individual assignment with a total of 40 marks. The allocation of marks is as follows:
Statistical Analysis (including excel) 30
Professional Report 10
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.
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.
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]
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