Bus708 The Relationship Between The Assessment Answers
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 2013-2014 individual 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 , 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 requirement 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 brief introduction about the assignment, including your research question. Include a short summary of a related article with a proper citation. b. 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. b. 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. b. 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?
b. 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: Calibri, 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.
Answer:
| Hypothesis Testing | Sw_amt ' Female' | Sw_amt 'Male' | |||||||
| 0 | 31304 | t-Test: Two-Sample Assuming Unequal Variances | |||||||
| Hypothesis Testing for occ code 7 | 86934 | 33935 | |||||||
| 28649 | 0 | Sw_amt ' Female' | Sw_amt 'Male' | ||||||
| p-value = | 0.0027 | 69620 | 20583 | Mean | 33841.718 | 48181.458 | |||
| 0 | 63835 | Variance | 1117454800.928 | 2196179023.112 | |||||
| Null Hypothesis: p = | 0.8 | 5485 | 203913 | Observations | 478.000 | 522.000 | |||
| Alternative Hypothesis: | Greater Than | 54301 | 0 | Hypothesized Mean Difference | 0.000 | ||||
| Number of Trials: n = | 46 | 37561 | 58188 | df | 943.000 | ||||
| Number of Successes = | 44 | 21017 | 33316 | t Stat | -5.605 | ||||
| pi-hat = | 0.9565 | 0 | 54609 | P(T<=t) one-tail | 0.000 | ||||
| 644 | 49195 | t Critical one-tail | 1.646 | ||||||
| 0 | 15445 | P(T<=t) two-tail | 0.000 | ||||||
| 32585 | 29038 | t Critical two-tail | 1.962 | ||||||
| 41926 | 0 | ||||||||
| 0 | 38684 | ||||||||
| 75239 | 59602 | ||||||||
| 0 | 59599 | ||||||||
| 104450 | 1717 | ||||||||
| 60246 | 36887 | ||||||||
| 59806 | 55236 | ||||||||
| 45217 | 22554 | ||||||||
| 0 | 7923 | ||||||||
| 5161 | 58445 | ||||||||
| 6029 | 43284 | ||||||||
| 88460 | 35604 | ||||||||
| 20704 | 0 | ||||||||
| 72646 | 0 | ||||||||
| 37240 | 4258 | ||||||||
| 40368 | 73286 | ||||||||
| Gender | Sw_amt | Sw_amt ' Female' | Sw_amt 'Male' | ||||||
| Female | 785631 | 785631 | 78524 | t-Test: Two-Sample Assuming Unequal Variances | |||||
| Female | 85214 | 85214 | 39752 | ||||||
| Female | 96214 | 96214 | 78642 | Sw_amt ' Female' | Sw_amt 'Male' | ||||
| Female | 63542 | 63542 | 96312 | Mean | 160065.875 | 117675.643 | |||
| Female | 0 | 0 | 73251 | Variance | 60804804920.117 | 38421545861.478 | |||
| Male | 78524 | 95842 | 65822 | Observations | 16.000 | 14.000 | |||
| Female | 95842 | 95123 | 65417 | Hypothesized Mean Difference | 0.000 | ||||
| Male | 39752 | 96845 | 85652 | df | 28.000 | ||||
| Male | 78642 | 49654 | 0 | t Stat | 0.524 | ||||
| Male | 96312 | 93215 | 98521 | P(T<=t) one-tail | 0.302 | ||||
| Female | 95123 | 65123 | 79631 | t Critical one-tail | 1.701 | ||||
| Female | 96845 | 789651 | 0 | P(T<=t) two-tail | 0.604 | ||||
| Male | 73251 | 74632 | 789621 | t Critical two-tail | 2.048 | ||||
| Female | 49654 | 75312 | 96314 | ||||||
| Male | 65822 | 78514 | |||||||
| Male | 65417 | 16542 | |||||||
| Male | 85652 | ||||||||
| Male | 0 | ||||||||
| Female | 93215 | ||||||||
| Female | 65123 | ||||||||
| Female | 789651 | ||||||||
| Female | 74632 | ||||||||
| Male | 98521 | ||||||||
| Female | 75312 | ||||||||
| Male | 79631 | ||||||||
| Female | 78514 | ||||||||
| Male | 0 | ||||||||
| Female | 16542 | ||||||||
| Male | 789621 | ||||||||
| Male | 96314 | ||||||||
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