ECON 1030 – BUSINESS STATISTICS 1: Individual Assignment Instructions: This is an individual assignment with a total of 40 marks. The allocation of marks is as follows: Statistical Analysis (including...

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ECON 1030 – BUSINESS STATISTICS 1: Individual Assignment
Instructions:
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
     XXXXXXXXXXTotal
    40
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 e
ors. 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:
https:
pursuit.unimelb.edu.au/articles/lost-in-transit
https:
about.unimelb.edu.au/newsroom/news/2019/july/hilda-examines-how-australians-are-balancing-work-and-home
Each question is described below XXXXXXXXXX XXXXXXXXXX = 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 co
elation 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 co
elation 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 ca
y 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 ca
y 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]
Answered Same DayOct 27, 2021ECON1030

Solution

Shubham answered on Oct 28 2021
47 Votes
Notes_start here
    Data overview:
    The data set is a random sample of 99 employees in Australia in 2017.
    Sources: the 2017 HILDA (Household, Income and Labour Dynamics in Australia) survey
    Variable Definition:
    psy_health    a scale number (ranging from 0 to 100) that captures a person's psychological health. The larger the number, the better the psychological health.
    age
    wage    salary per hour measured in 2017 AU$
    commute    time in hours spent on travelling to and from work per day
Data
    psy_health    age    wage    commute        Descriptive Statistics
    72    33    35.18518    0.25
    84    21    16.26667    0.2        psy_health            age            wage            commute
    40    58    45.13044    6
    84    34    37.09302    1        Mean    69.3333333333        Mean    37.7373737374        Mean    36.0589916465        Mean    1.366690696
    80    23    38.15789    0.6        Standard E
or    1.994426371        Standard E
or    1.4317126978        Standard E
or    1.8867566334        Standard E
or    0.1052014163
    88    63    28    2        Median    76        Median    34        Median    33.575        Median    1
    64    61    41.66667    0.5        Mode    80        Mode    22        Mode    37.5        Mode    2
    88    53    41.6    1.25        Standard Deviation    19.8442918334        Standard Deviation    14.2453614782        Standard Deviation    18.7729914707        Standard Deviation    1.0467408757
    88    45    41.36666    1        Sample Variance    393.7959183673        Sample Variance    202.9303236446        Sample Variance    352.425208758        Sample Variance    1.095666461
    76    18    16.32143    0.5        Kurtosis    0.6886480236        Kurtosis    -1.1596868619        Kurtosis    4.0154127215        Kurtosis    4.2295855052
    88    22    31    1.75        Skewness    -0.9288915425        Skewness    0.3655510972        Skewness    1.6247876881        Skewness    1.6590961214
    60    39    24.56    0.5        Range    88        Range    46        Range    109.749967        Range    5.8571429
    80    59    115.0833    0.3333333        Minimum    12        Minimum    18        Minimum    5.333333        Minimum    0.1428571
    48    30    40.04546    1.75        Maximum    100        Maximum    64        Maximum    115.0833        Maximum    6
    92    55    54.6    1.5        Sum    6864        Sum    3736        Sum    3569.840173        Sum    135.3023789
    52    64    91.17647    1        Count    99        Count    99        Count    99        Count    99
    48    30    24.95    1.666667
    68    41    22.64151    0.25
    80    26    38.85185    1
    84    22    25.66667    1
    76    23    19.18182    1.333333
    68    22    40    1
    56    43    33.575    1.5
    60    60    100.1556    0.8333333
    88    36    61.36666    2
    80    36    48.2    2
    92    29    39.5    2
    56    32    32.34375    0.1428571
    60    27    21.81818    0.4
    80    33    37.6    1
    68    55    39.85    2
    88    20    30.32    0.3333333
    80    37    37.5    1
    16    22    25    1.5
    48    44    28.33333    2.4
    80    40    41.45    2
    80    63    23.4375    2.5
    80    61    45    1.333333
    88    26    21.81818    0.3333333
    56    40    5.333333    3.666667
    32    31    46.03333    0.8333333
    92    23    33.33333    2
    40    19    25.75    0.3333333
    96    59    16.775    0.2857143
    80    37    26.11539    1
    88    56    29.16667    0.3333333
    12    26    36.07143    3
    68    41    9.2    3.333333
    64    33    57.14286    1.333333
    76    45    40.75    0.25
    100    59    62.98077    2
    96    18    17.38095    0.2
    16    20    12.55263    0.25
    88    42    40.46875    2.333333
    72    58    50.35    2.5
    96    64    36    2
    52    20    37.40625    1.25
    76    20    19    0.3333333
    68    22    24.95    0.5
    48    33    29.36842    0.4
    76    56    31.26087    1
    76    29    25.625    2
    56    20    16.11539    2
    92    26    21.66667    0.5
    60    56    26.61111    0.3333333
    48    41    48.71429    0.6666667
    96    58    30.36842    0.4
    36    44    18.33333    5
    32    18    14.28571    3
    60    34    17.5    1.25
    56    36    48.75    2
    92    51    52.17391    2
    44    27    7    1
    64    39    85.35    1.333333
    40    27    32.4    1.666667
    80    30    24.25    0.3333333
    80    46    24.96875    2
    88    45    46.875    1
    60    29    33.75    1
    68    51    75    1
    80    31    34.04255    4.166667
    92    54    37.5    1
    76    19    15.625    1
    60    19    29.16667    0.5
    76    28    20    1.333333
    84    31    43.75    2
    76    62    69.04    1.333333
    56    59    17.16    3.333333
    12    28    24.19444    2
    52    24    37.5    1.5
    64    53    64.44    0.1428571
    100    28    32.6087    1
    72    28    56.25    2
    72    48    21.05263    2
    76    34    50    0.5
    80    44    38.18182    0.7142857
    68    54    43.84211    2
    68    18    24.54545    1
    72    39    38    0.5
Graphs
    psy_health    age    wage    commute
    72    33    35.18518    0.25
    84    21    16.26667    0.2
    40    58    45.13044    6
    84    34    37.09302    1
    80    23    38.15789    0.6
    88    63    28    2
    64    61    41.66667    0.5
    88    53    41.6    1.25
    88    45    41.36666    1
    76    18    16.32143    0.5
    88    22    31    1.75
    60    39    24.56    0.5
    80    59    115.0833    0.3333333
    48    30    40.04546    1.75
    92    55    54.6    1.5
    52    64    91.17647    1
    48    30    24.95    1.666667
    68    41    22.64151    0.25
    80    26    38.85185    1
    84    22    25.66667    1
    76    23    19.18182    1.333333
    68    22    40    1
    56    43    33.575    1.5
    60    60    100.1556    0.8333333
    88    36    61.36666    2
    80    36    48.2    2
    92    29    39.5    2
    56    32    32.34375    0.1428571
    60    27    21.81818    0.4
    80    33    37.6    1
    68    55    39.85    2
    88    20    30.32    0.3333333
    80    37    37.5    1
    16    22    25    1.5
    48    44    28.33333    2.4
    80    40    41.45    2
    80    63    23.4375    2.5
    80    61    45    1.333333
    88    26    21.81818    0.3333333
    56    40    5.333333    3.666667
    32    31    46.03333    0.8333333
    92    23    33.33333    2
    40    19    25.75    0.3333333
    96    59    16.775    0.2857143
    80    37    26.11539    1
    88    56    29.16667    0.3333333
    12    26    36.07143    3
    68    41    9.2    3.333333
    64    33    57.14286    1.333333
    76    45    40.75    0.25
    100    59    62.98077    2
    96    18    17.38095    0.2
    16    20    12.55263    0.25
    88    42    40.46875    2.333333
    72    58    50.35    2.5
    96    64    36    2
    52    20    37.40625    1.25
    76    20    19    0.3333333
    68    22    24.95    0.5
    48    33    29.36842    0.4
    76    56    31.26087    1
    76    29    25.625    2
    56    20    16.11539    2
    92    26    21.66667    0.5
    60    56    26.61111    0.3333333
    48    41    48.71429    0.6666667
    96    58    30.36842    0.4
    36    44    18.33333    5
    32    18    14.28571    3
    60    34    17.5    1.25
    56    36    48.75    2
    92    51    52.17391    2
    44    27    7    1
    64    39    85.35    1.333333
    40    27    32.4    1.666667
    80    30    24.25    0.3333333
    80    46    24.96875    2
    88    45    46.875    1
    60    29    33.75    1
    68    51    75    1
    80    31    34.04255    4.166667
    92    54    37.5    1
    76    19    15.625    1
    60    19    29.16667    0.5
    76    28    20    1.333333
    84    31    43.75    2
    76    62    69.04    1.333333
    56    59    17.16    3.333333
    12    28    24.19444    2
    52    24    37.5    1.5
    64    53    64.44    0.1428571
    100    28    32.6087    1
    72    28    56.25    2
    72    48    21.05263    2
    76    34    50    0.5
    80    44    38.18182    0.7142857
    68    54    43.84211    2
    68    18    24.54545    1
    72    39    38    0.5
Relationship between psychological health and commuting...
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