Statistical Modelling - Assignment PDF
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Added on 2021-06-14
Statistical Modelling - Assignment PDF
Added on 2021-06-14
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STATISTICALMODELLINGSTUDENT ID:[Pick the date]
Section I: Introductiona)Currently, more women are taking up jobs that were previously reserved for men;however, there’s still a significant difference in income distribution between the maleand female workers. The Workplace Gender Equality Agency (WGEA) estimates thatthe average wage of female workers is nearly 15% lower that of their male colleagues.Notably, the gap in gender disparities is evident in to the occupations that aredominated by males and also those that are considered to be female-centric.Nonetheless, the government has put in place measures to ensure gender equality inthe workplace implying the need to carry out investigative research with the aim ofoutlining the necessary measures to improve the current situation. As such, theobjective of the research is to determine the difference in average salary between menand women. At the same time, the study will gather information regarding the variousoccupations.b)The first dataset presents the demographic information including: participants’gender, occupation, wages/salary, and the related gift deductions for 1000 taxpayerswere selected randomly. The dataset one is secondary and is borrowed from theAustralian Tax Office (ATO). The gender variable is a nominal scale and isrepresented by male and female labels. The taxpayers’ occupation is represented incodes arranged in an orderly manner and is also a nominal variable. Annual salariesand Wages are given as qualitative data represented as ration scales. Moreover, thegift deductions for each taxpayer are a quantitative variable captured using the ratioscale. Table 1.1 below presents the first five varibles from dataset 1.Table 1.1 Research Variables c)The second dataset is collected using convenience sampling. Pre-selected participantswere contacted using phone calls. The participants represented both gender, male andfemale. Information about their salaries and wages was collected. This dataset has the
potential for shortcomings as the data collected may be influenced by researcher biasbecause of using the convenience sampling method. Furthermore, the professions arenot matched to their occupation. It’s also possible that data collected could beinaccurate since people with lower wages may inflate their income because ofpersonal embarrassment or other unstated reasons. The data gathered in this case isprimary as it is the original work of the researcher and has not been borrowed fromprevious publications. Basically, two variables are of importance in this dataset,namely: gender and salary/wages. The gender variable is labelled as male or femaleand is categorical variable. The salary/wage variable is a nominal variable and iscomputed on the variable scale. Dataset 1 is made up of a sample size of 30participants. Section II: Descriptive Statistics from Dataset 1a)Figure 2.1 below presents the graphical relationship between gender and occupationfrom the first dataset. Figure 2.1 Relationship between gender and occupationFrom the graph, it is evident that all the professions represented in the study have no genderbalance as some have more men than women and vice versa. The difference in genderrepresentation is typically influenced by the nature of the occupation. For example,
occupation code number 7 represents drivers and machine operators. It is clear from thegraph that there are more male workers in this occupation than females. On the contrary,occupation codes 4 (Community and Personal Service Workers) and 5 (Clerical andAdministrative workers) are represented by more females than males presumably because ofthe nature of the task which augers well with a female. Clerical jobs are usually characterisedby desktop activities which are mostly preferred by women. On the other hand, communityworkers are required to have a higher degree of empathy leading to a higher occupation byfemales. Subsequently, the results from the graph indicate an observable trend in occupationby gender. b)Figure 2.2 represents the relationship between gender and salary/wageFigure 2.2 The bar chart that is figure two represents the gender gap that was aforementionedin the introductory section of this report. From the graph, women are surpassing men as wereach the 50,000 salaries/wage mark indicating that more women are employed at lowersalaries than men. On the other hand, as the salary levels increase beyond the 50,000 mark, aclear overrepresentation of men is evidence indicating a direct relationship between malesand salary/wages. The proportion of female representation reduces as the salary increasesindicating an inverse relationship between females and salaries. As such, it could be inferredthat men tend to dominate occupations with higher compensations. However, this argumentmay not be tenable as previous studies have reported a disparity in payment even in
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