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Assessment 3: Individual Written Report DueWednesdayby23:59 Points40 Submittinga text entry box or a file upload Available9 May at 15: XXXXXXXXXXJun at 23:59about 1 month Assessment Details: Given a...

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Assessment 3: Individual Written Report

  • DueWednesdayby23:59
  • Points40
  • Submittinga text entry box or a file upload
  • Available9 May at 15: XXXXXXXXXXJun at 23:59about 1 month

Assessment Details:

Given a data set that replicates the industry/world, students will be required to perform a wide range of statistical analysis covered in the course, with focus on the analysis of relationships between variables.

The response to the assignment must be provided in the form of a business report with no more than 10 pages (excluding cover page). The structure of your business report must include:

  1. A Title
  2. An Executive Summary
  3. An Introduction
  4. Analysis
  5. Conclusions

Group Project Data:

IndividualBusStats.xlsxDownload IndividualBusStats.xlsx

Individual Assignment Instructions - MEL- Sem XXXXXXXXXXdocxDownload Individual Assignment Instructions - MEL- Sem XXXXXXXXXXdocx

Report Template.docxDownload Report Template.docx

HE_Assignment_cover_sheet.docxDownload HE_Assignment_cover_sheet.docx


The assignment is due onWednesday 8 June 2022 at 23:59 PM(Melbourne Time). Hard copies will not be accepted. Your work should be submitted via Canvas using Turnitin.

You can submit the documents separately (cover sheet, Report, Excel), or you can combine the coversheet and the word document into one single document, and submit together with the Excel file.

Late work will be penalised using RMIT protocols as noted in the ECON1030 course guide.

I declare that in submitting all work for this assessment I have read, understood and agree to the content and expectations of theAssessment declaration.

Common Queries on Assignment

What are the relevant lecture topics covered in the final assessment?
This assessment focuses on the relationship between variables. Thus, the relevant lecture topics are: the 2nd half of Topic 9, Topic 10 and Topic 11. You also need skills you learned in Topic 1.

Unlike in assessment 2, hereyouwill NOT separatemale and female workers into 2 sub-samples. The analysis will be done on the entire sample.

Do I need to format a number on Excel that appears in exponential notation?
You don’t have to, but you are more likely to interpret the exponential notation correctly if you convert it to a number.
To convert an exponential notation to a number:
Select the cell with the numbers and right-click on them and click Format Cells
Go to the Number tab and click ok.
For example,
2.00E+05 =2×10^5=200000
2.00E-05 =2×10^(-5)= XXXXXXXXXX ~ 0

Do I need to remove outliers from the dataset?

No, you will not remove outliers.

How do I read t-value from Table E.3/E.4 if the degree of freedom (df) is larger than 120?
For any df>120, read t-values from the last row eg ∞.

Question 5:
What is this question about?
1) compare the magnitude of the gender coefficients from both models;
2) explain why the coefficients are different. How is a simple regression different from a multiple regression? Think about the interpretation of a slope coeff in a multiple regression. What does “keeping everything else constant” mean here when you interpret the slope coeff for “male”? Thinking about how gender wage discrimination is defined, how this slope parameter relates to gender discrimination?

Answered 4 days After Jun 03, 2022


Dr Shweta answered on Jun 07 2022
91 Votes

Executive Summary
Regression analysis is a potent statistical tool that helps us in analyzing the relationship between two or more desired variables and examines the influence of one or more independent variables on a particular dependent variable. In this report a survey of earning of Australian workers and their education level, skills and working experience was collected and analyzed via regression analysis to identify the reason of difference in their earnings. We used regression analysis to assess the effects of these collected variables on their earnings via designing of two models- In model A, we compare the data of working male and female and observed that more males are working 673 as compared to 428 female which shows a marked gender’s difference at the level of worker’s selection. When earnings of Australian males and females were compared via line graph of MS-excel, we found that the data points of female earnings were lower than that of the male earnings in the line graph drawn. Similarly, in simple linear regression analysis, we obtained negative slope which confirms that the low earnings of females. All of these results concluded that males have more earnings than female and there is demarcated gender discrimination among earnings of working population in Australia. In model B, we compared the difference in earnings of males and females’ workers in terms of different variables- the educational level, skill and experience via comparative line graphs designed using MS-Excel. We found that females or males who have high education, good skills and high experience earn more money while others having low education, less skills and less experience earns low. The validity of the model was analyzed via multiple regression analysis with a null hypothesis that these parameters have no difference on the amount of money earned. We found the slope coefficient of 0.98 and R2 value of 97% and p value of 0.0165 in two tailed analysis. All these values are statistically significant since the p value is less than 0.05 and confirms that gender, education level, skills and working experience has strong impact on the earnings of working population of Australia and improving these parameter’s will significantly improve their living standard and economic growth.
Earnings is essential for everyone’s survival since it is essential for buying the things of daily necessities like food, water, electricity, clothes etc. and for availing the essential services like education, health facilities, recreational amenities etc. Therefore, everyone wish to earn i
espective of their gender to fulfill their needs and dreams. Still, Gender inequality is a key issue in Australia which influences the unde
epresentation of female abilities and power. Various studied support this fact for instance kulik et al (2021) reported about the gender inequality reflected in terms of hiring
etention rates, occupational segregation, differences in leadership roles etc. and mentioned that it has many negative consequences on country’s growth, economy and financial performance,
Similarly, Birkelund et al in 2021 reported that there is a complex issue of gender inequality exists at the level of hiring process and hence this gender discrimination is basically responsible for driving the women’s in the labour market since when men and women both with same education level, skills and working experience apply for the same jobs, then males are prefe
ed not women. Women are discriminated as per the employer’s decision who believes males are more efficient and has more working capabilities than female. However Latimer et al in 2019 suggested that various strategies and planning’s are made in Australia to solve this complex of gender’s inequality. Women education is putting at higher priority and specials reservations are made for their placements at the deserving positions.
To assess this gender inequality as mentioned in various studies and reports and to check the financial similarity between males and females among working population of Australia a survey was selected in a local region and earnings details, education level, skills and working experience details of 1099 full-time Australian workers of both genders belonging to the age group 20-74 was recorded.
To assess the discrimination in earning of workers on the basis of their gender and difference in the amount of money earned on the basis of their different level of education, working experience and skills statistical analysis was performed. It is a suitable method of mathematically sorting out the useful variables by assessing their impact on the desired variable. The importance of the linear and multiple regression analysis is that it helps us in...

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