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Microsoft Word - BUS XXXXXXXXXXAssignment Description draft 3 BUS708 Statistics and Data Analysis Statistical Modelling Assignment Trimester 1, 2018 1 OVERVIEW OF THE ASSIGNMENT This assignment will...

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Microsoft Word - BUS XXXXXXXXXXAssignment Description draft 3
BUS708 Statistics and Data Analysis
Statistical Modelling Assignment
Trimester 1, 2018
1 OVERVIEW OF THE ASSIGNMENT
This assignment will test your skill to collect and analyse data to answer a specific business problem.
It will also test your understanding and skill to use statistical methods to make inferences about
usiness data and solve business problems, including constructing hypotheses, test them and
interpret the findings.
Gender gap is the difference between the salary of men and the salary of women. The reasons of
gender gap are not only because of discrimination in hiring, but also includes the different industries
that women and men are working, as well as many other reasons. By using an edited subset of the
sample file from the Australian Taxation Office (ATO), your task is to summarise and analyse several
aspects of the salary and occupation of the different gender. In addition, you are also asked to suggest
one relevant research question and then collect and analyse a dataset that will answer your research
question.
2 TASK DESCRIPTION: WRITTEN REPORT
There are two datasets involved in this assignment: Dataset 1 and Dataset 2, detailed below.
Dataset 1: You will receive an email that contains a dataset that is specifically allocated to you. This
dataset is a subset of XXXXXXXXXXindividual sample file, provided by the ATO and has been edited to
only include a subset of the cases and variables. The original dataset can be obtained from
https:
data.gov.au/dataset/taxation-statistics-individual-sample-files, and it is under the license of
Creative Commons Attribution 3.0 Australia. Data dictionary of the edited dataset is given in the
following table.
Variable Description Values
Gender Gender (sex) Female or Male
Occ_code Salary/wage occupation
code
0 = Occupation not listed/ Occupation not specified
1 = Managers
2 = Professionals
3 = Technicians and Trades Workers
4 = Community and Personal Service Workers
5 = Clerical and Administrative Workers
6 = Sales workers
7 = Machinery operators and drivers
8 = Labourers
9 = Consultants, apprentices and type not specified or
not listed
Sw_amt Salary/wage amount All numeric
Gift_amt Gifts or donation deductions All numeric
Dataset 2: Collect data (e.g. via a survey) that will answer your research question. There is no
equirement about the number of variables, sampling methods and sample size, but you need to
justify your approaches in Section 1 (see below).
Both datasets should be saved in an Excel file (one file, separate worksheets). All data processing
should be performed in Excel or Statkey (http:
www.lock5stat.com/StatKey).
Prepare a report in a document file (.doc or .docx) which includes all relevant tables and figures, using
the following structure:
1. Section 1: Introduction
a. Give a
ief introduction about the assignment, including your research question.
Include a short summary of a related article with a proper citation.
. Dataset 1: Give a short description about this dataset. Is this primary or secondary
data? What types of variable(s) is involved? Display the first 5 cases of your dataset.
c. Dataset 2: Explain how you collect the data and discuss its limitation (e.g. whether
your sample is biased). Is this primary or secondary data? What type of variable(s)
is/are involved? You don’t need to display your data in this section.
2. Section 2: Descriptive Statistics
Use Dataset 1
a. Using suitable graphical display, describe the relationship between the variables
Gender and Occ_code for Dataset 1. Make sure your graph shows the distribution of
Gender for each Occ_code.
. Using suitable graphical display, describe the relationship between the variables
Gender and Sw_amt.
c. Using suitable numerical summary, describe the relationship between the variables
Gender and Sw_amt.
d. Using suitable graphical display, describe the relationship between the variables
Sw_amt and Gift_amt.
3. Section 3: Inferential Statistics
Use Dataset 1
a. List top 4 occupation based on median salary and find the proportion of the gender
of those top 4 occupation.
. Perform a suitable hypothesis test at a 5% level of significance to test whether the
proportion of machinery operators and drivers who are male is more than 80%.
c. Perform a suitable hypothesis test at a 5% level of significance to test whether there
is a difference in salary amount between gender.
Use Dataset 2
d. Perform a suitable statistical analysis on dataset 2 (the one you collected) that will
answer your research question.
4. Section 4: Discussion & Conclusion
a. What can you conclude from your findings in the previous sections?
. Give a suggestion for further research
3 TASK DESCRIPTION: PRESENTATION/INTERVIEW
A presentation/interview for the assignment is scheduled on Week 11, in your allocated tutorial.
You do NOT need to prepare a presentation material (e.g. power-point slides), instead, you will be
asked to demonstrate and/or explain how you summarised the data and how you performed the
analysis. You may be asked to reproduce what you have made in your written report (e.g. generate a
chart or numerical summary using Excel or Statkey).
4 SUBMISSION REQUIREMENT
Deadline to submit written report: Week 10 Wednesday (23 May 2018), 5pm
You need to submit 2 files to Turnitin:
1. Main report, in a Microsoft Word document file (this is the file that will be marked, it should
contain all necessary tables and figures)
2. Dataset, in a Microsoft Excel file (this is just a supporting file)
Main report (word document):
1. Size: A4
2. Use Assignment Cover Page (download from Moodle) with your details and signature
3. Single space
4. Font: Cali
i, 11pt
Dataset (excel document):
1. Dataset 1 in Sheet 1
2. Dataset 2 in Sheet 2
3. Data processing for each section in other sheets (rename the sheet appropriately)
5 DEDUCTION, LATE SUBMISSION AND EXTENSION
Late submission penalty: - 5% of the total available marks per calendar day unless an extension is
approved. For extension application procedure, please refer to Section 3.3 of the Subject Outline.
6 PLAGIARISM
Please read Section 3.4 Plagiarism and Referencing, from the Subject Outline. Below is part of the
statement:
“Students plagiarising run the risk of severe penalties ranging from a reduction through to 0 marks for a first
offence for a single assessment task, to exclusion from KOI in the most serious repeat cases. Exclusion has
serious visa implications.”
“Authorship is also an issue under Plagiarism – KOI expects students to submit their own original work in both
assessment and exams, or the original work of their group in the case of a group project. All students agree to a
statement of authorship when submitting assessments online via Moodle, stating that the work submitted is
their own original work.
The following are examples of academic misconduct and can attract severe penalties:
 Handing in work created by someone else (without acknowledgement), whether copied from another
student, written by someone else, or from any published or electronic source, is fraud, and falls under
the general Plagiarism guidelines.
 Students who willingly allow another student to copy their work in any assessment may be considered
to assisting in copying/cheating, and similar penalties may be applied. ”

BUS XXXXXXXXXXT1 Ass Marking Scheme.xlsx
Section Mark Criteria Question
Section 1: Introduction
1.a. 5
Clear and concise intro: 2
Research questions: 1
Proper citation: 1
A summary of a related article: 1
a XXXXXXXXXXGive a
ief introduction about the assignment, including your research
question. Include a short summary of a related article with a proper citation.
1.b. 5
Clear description: 2
Primary/secondary: 1
Types of variables: 1
Display first 5 cases: 1
XXXXXXXXXXDataset 1: Give a short description about this dataset. Is this primary or
secondary data? What types of variable(s) is involved? Display the first 5 cases of
your dataset.
1.c. 5
Clear data collection description: 2
Limitation: 1
Primary/secondary: 1
Types of variables: 1
c XXXXXXXXXXDataset 2: Explain how you collect the data and discuss its limitation (e.g.
whether your sample is biased). Is this primary or secondary data? What type of
variable(s) is/are involved? You don’t need to display your data in this section.
Section 2: Descriptive Statistics
2.a. 5
Co
ect choice of graph: 1
Co
ect graph based on data: 1
Title/label/legends: 1
Comments: 2
a XXXXXXXXXXUsing suitable graphical display, describe the relationship between the
variables Gender and Occ_code for Dataset 1. Make sure your graph shows the
distribution of Gender for each Occ_code.
2.b. 5
Co
ect choice of graph: 1
Co
ect graph based on data: 1
Title/label/legends: 1
Comments: 2
XXXXXXXXXXUsing suitable graphical display, describe the relationship between the
variables Gender and Sw_amt for Dataset 1.
2.c. 5
Co
ect centre (mean/median): 2
Co
ect spread (stddev/IQR): 2
Comments: 1
c XXXXXXXXXXUsing suitable numerical summary, describe the relationship between the
variables Gender and Sw_amt for Dataset 1.
2.d. 5
Co
ect choice of graph: 1
Co
ect graph based on data: 1
Title/label/legends: 1
Comments: 2
d XXXXXXXXXXUsing suitable graphical display, describe the relationship between the
variables Sw_amt and Gift_amt.
Section 3: Inferential Statistics
3.a. 5
Co
ect list: 1
Co
ect proportions: 2
Comments: 2
a. List top 4 occupation based on median salary and find the proportion of the
gender of those top 4 occupation.
3.b. 5
Co
ect hypothesis: 1
Co
ect test-stat: 1
Co
ect p-value: 1
Co
ect conclusion: 2
. Perform a suitable hypothesis test at a 5% level of significance to test
whether the proportion of machinery operators and drivers who are male is more
than 80%.
3.c. 5
Co
ect hypothesis: 1
Co
ect test-stat: 1
Co
ect p-value: 1
Co
ect conclusion: 2
c. Perform a suitable hypothesis test at a 5% level of significance to test whether
there is a difference in salary amount between gender.
3.d 5
Suitable choice of statistical method: 1
Co
ect step of statistical method: 2
Comments: 2
d XXXXXXXXXXPerform a suitable statistical analysis
Answered Same Day May 07, 2020 BUS708 University of the Sunshine Coast

Solution

Pooja answered on May 09 2020
134 Votes
Section 1 Introduction
Part a
The main objective is to analyze the salary amount on the basis of gender and occupation code. Gift or donation deductions is also considered. The numeric in graphical representation helps to understand the relationship of salary amount with gender, occupation code, and gift amount. The top four occupations are identified on the basis of median salary. The proportion of gender in the stock for occupation is analyzed. Hypothesis testing is used to test whether the proportion of machinery operators and drivers who are male is more than 80%. Another area of concern is to know if there is any significant difference in the salary amount between males and females.
The objective of the second data set is to analyze the linear relationship between age and blood pressure.
Part
The data set 1 contains 4 variables namely gender, occupation code, salary amount, and gift deduction. This data is obtained by the method of secondary research. Gender and occupation code is measured by the nominal scale of measurement. Gender is categorized as either male or female. Occupation code has 10 categories namely occupation not listed (0), manager (1), professional (2), technical and trade worker (3), community and personal service worker (4), clerical and administrative worker (5), sales worker (6), machinery operator and driver (7), laborers (8), consultant apprentices and type not specified or not listed (9). Salary amount and give deductions are numeric variables which are measured by ratio scale of measurement. A display of first 5 cases of the data is given below.
    Gende
    Occ_code
    Sw_amt
    Gift_amt
    Female
    2
    32733
    0
    Female
    5
    13445
    0
    Female
    1
    50507
    109
    Male
    0
    0
    0
    Female
    9
    20489
    0
Part c
The second data that is collected by the method of primary research. A survey regarding Gender, age and blood pressure is social media platform (Facebook and LinkedIn). The response of 50 individuals which is randomly selected is recorded on Google Drive. Gender is a nominal variable which has two categories of either male or female. Age and blood pressure is measured by ratio scale of measurement.
Section 2: Descriptive Statistics
Part a
The bar graph is an appropriate display to know the relationship between Gender and occupation code. The bar graph is an appropriate graphical representation as both the variables are measured by the nominal scale of measurement. Kuzma, J. W., & Bohnenblust, S. E. (1992). 
The proportion of males is comparatively higher with occupation code occupation not listed (0), manager (1), technical and trade worker (3), machinery operator and driver (7), laborers (8), consultant apprentices and type not specified or not listed (9). The proportion of females is comparatively higher with occupation code community and personal service worker (4), clerical and administrative worker (5), sales worker (6). There is not a much significant difference in the proportion of males and females with occupation code as professional (2).
Part
The bar chart representing the average salary amount for male and female is given below.
It is evident that average salary amount for males is comparatively higher as compared to females.
Part c
The descriptive statistics for salary amount for males and females is given below.
     
    Sw_amt_Female
    Sw_amt_male
    Mean
    31768.50976
    57830.74026
    Standard E
o
    1518.495799
    2886.24626
    Median
    26895
    46312
    Mode
    0
    0
    Standard Deviation
    32603.48748
    67008.17127
    Sample Variance
    1062987396
    4490095017
    Kurtosis
    11.39628327
    35.94743051
    Skewness
    2.118070349
    4.042823291
    Range
    308183
    839840
    Minimum
    0
    0
    Maximum
    308183
    839840
    Sum
    14645283
    31170769
    Count
    461
    539
The average salary for males is comparatively high with the value of $57830.740 as compared to that of females with the value of $31768.50. The high value of standard deviation for both male and female salary is an indication that their mean is not reliable. Hatcher, L. (2013). 
From the value of skewness, I can say that the distribution of salary for both males and females is positively skewed. This indicates that there are very few meals as well as females with high salary. For skewed distribution, the median is considered as the best measure of Central Tendency. The median salary for male and female is 26895 dollars and 46312 dollars respectively. Hatcher, L. (2013). 
Part d
For two variables measured by ratio scale of measurement, a scatter plot is an appropriate display. The scatter plots for salary and gift deduction is given below.
There is almost no linear relationship between salary amount and gift deduction. All points are random with no specific direction. Samuels, M. L., Witmer, J. A., & Schaffner, A. A. (2012). 
Section 3: Inferential Statistics
Part a
The top four occupation on the basis of median salary is listed in the table below.
    Top 4 occupations based on median salary
    2
    Professionals
    1
    Managers
    3
    Technicians and Trades Workers
    7
    Machinery operators and drivers
The proportion of gender considering these top four occupations is summarized in the table below.
    Count of Gende
    Column Labels
    
    
    
    
    Row Labels
    1
    2
    3
    7
    Grand Total
    Female
    39.02%
    48.73%
    15.69%
    7.14%
    33.33%
    Male
    60.98%
    51.27%
    84.31%
    92.86%
    66.67%
    Grand Total
    100.00%
    100.00%
    100.00%
    100.00%
    100.00%
The proportion of males is higher as compared to females for the occupation of Managers, Technicians and Trades Workers and machinery...
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