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:
A Title
An Executive Summary
An Introduction
Analysis
Conclusions
RMIT Classification: Trusted ECON 1035 – BUSINESS STATISTICS 1 Assessment 3: Individual Assignment Instructions: This is an individual assignment with a total of 40 marks. The allocation of marks is as follows: Statistical Analysis: 32 (including Excel) Professional Report: 8 Total:40 The response to the assignment must be provided in the form of a professional report with no more than 10 pages (excluding cover page). The structure of your professional report must include: 1] A Title, 2] An Executive Summary, 3] An Introduction, 4] Analysis, 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. The Excel workbook you submit needs to be clear and carefully organised. It will be treated as an appendix to your report, i.e. not included in the page count. You will need to take the relevant results from your Excel workbook and incorporate into your report. Do not refer to the Excel workbook within the Professional report. The Report needs to be standalone. 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 errors. Where relevant, round to 3 decimal places. 5. With all relevant tables and charts, the report should be no more than 10 pages long. Problem Description: This is a further analysis of the gender pay gap in the Australian population. According to a recent report by KPMG Consulting, gender discrimination continues to be the single largest factor contributing to the gender pay gap (KPMG, 2019). In order to estimate the extent of discrimination in the job market where women with identical labour market characteristics as their male counterparts receive different wages, you will estimate a set of linear regression models. Since this is an additional analysis on the gender pay gap, the content in the Introduction section of your report may overlap with the one in the Group Assignment. However, you are encouraged to develop/source new background materials. You will use the same dataset as in Assignment 2. The data are drawn from the 2017 Household, Income and Labour Dynamics in Australia (HILDA) survey. The sample used for analysis comprises 824 full-time Australian workers in the age group 20-74. The dataset contains the following information: 1. Worker’s earnings: weekly earnings in 1000 AU dollars of full-time workers. [note the unit of measurement] 1. Gender: the dummy variable male = 1 if the individual is a male, and = 0 for a female. 1. Educational attainment: the dummy variable degree = 1 if the individual has a bachelor degree or higher qualification, and = 0 for lower than degree qualifications. 1. Skill level: the dummy variable skill = 1 if the individual is highly skilled, and = 0 if not highly skilled. 1. Experience: number of years of work experience. [Marks distribution: 5 + 6 + 9 + 2 + 5 + 2 + 3 = 32 marks; professional report = 8 marks] Locate the data file (IndividualBusStats.xls) on CANVAS. 1. Before estimating the regression equation, conduct a preliminary analysis of the relationship between workers’ earnings and 1) gender; 2) educational attainment; 3) skill level; and 4) experience. Use tables and/or appropriate graphs for the categorical variables (male, degree, skill) and the continuous variable (experience). Interpret your findings by answering the following questions: how much more/less does a male worker earn compared to a female worker? how much more/less does a degree holder earn versus a non degree holder? How much more/less does a highly skilled worker earn versus a worker who is not highly skilled? What kind of relationship do you observe between workers’ earnings and experience? (5 marks) 2. Use a simple linear regression to estimate the relationship between workers’ earnings (Y) and gender (X) (Model A). You may use the Data Analysis Tool Pack. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope coefficient. Carry out any relevant two-tailed hypothesis test of the slope coefficient using the critical value approach, at the 5% significance level, showing the step by step workings/diagram in your report. Interpret your hypothesis test results. (6 marks) 3. Now use a multiple regression model to explore the relationship of workers’ earnings (Y) with, gender (X1), educational attainment (X2), skill level (X3) and work experience (X4) (Model B). You may use Data Analysis Tool Pack for this. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope coefficients. Carry out any relevant two-tailed hypothesis tests for each individual slope coefficient using the p-value approach, at the 5% significance level. Carry out an overall significance test using the p-value approach. Carefully interpret your hypothesis test results. (9 marks) 4. Interpret the R-squared in Model A and adjusted R-squared in Model B. Which one is a better model? Why? (2 marks) 5. Compare the coefficient of gender in Model A and Model B. Explain carefully why the results are different, relating your discussion to gender discrimination. (5 marks) 6. Predict the earnings of a male worker who has a university degree, is highly skilled and has 10 years of work experience. Next predict the earnings of a female worker with the same characteristics. (2 marks) 7. If you could request additional data to study the factors that influence workers’ earnings, what extra variables would you request? Discuss two such variables, explaining why you choose them and how each of your proposed variables could be measured in the regression model. [You could draw evidence from journal articles, newspapers, etc] (3 marks) References: KPMG Consulting. (2016). She's price(d)less: The economics of the gender pay gap
Notes Data overview: The dataset consists of a random sample of 824 full-time Australian workers in the age group 20-74, in 2017. Sources: 2017 Household, Income and Labour Dynamics in Australia(HILDA) Survey The worksheet 'data' contains the dataset. Variable Definition: earningsweekly earnings of full-time Australian workers in AU$ malemale=1, female=0 degreedegree or higher qualification=1, lower than degree qualifications=0 skillhighly skilled=1, not highly skilled=0 experiencenumber of years of job experience &"Calibri"&12&KEEDC00RMIT Classification: Trusted&1# data earningsmaledegreeskillexperience 184101110 15000104 7500102 161800031 9090006 26850106 15000116 82500011 112800130 184101110 100001125 75000010 13150116 248901016 216701024 140601016 140001016 12500103 85001114 172600020 11970104 14310112 165001130 73000020 12080011 7200002 7970005 163400110 14910104 174500025 10000002 143500010 31500016 10000112 115000010 9310001 9000005 91400036 12500007 12000101 109300019 150001028 25350114 16110111 11000011 12970105 12500112 176401019 105000010 145000017 49870113 264700010 12470016 11890112 700001118 645001023 249301124 132000130 8600001 102100013 70000022 17200001 7800007 121600125 11510009 81300016 136001025 11270005 17320012 8500002 10940003 10930102 22500003 11400002 42000016 265001115 15630105 15000013 17260112 16000116 156801110 124101038 110500111 95000120 12470115 230101124 8400008 10720009 6930102 9000003 8750106 90000010 205001030 17550102 99700013 190401012 75000011 160001110 90000012 120500025 9530002 17500006 12280001 11000004 16000005 90800014 8810001 150000020 120000011 316401017 209101012 7800003 151901021 230001125 78600015 12500018 9000008 159601015 135000018 28770112 110001015 13810005 120001019 9700003 10000106 22000003 119600015 11320015 10000107 140001110 9400002 122700010 91000021 21000116 12950008 9500004 356701114 14400014 9620005 110000016 140001023 288701120 17790014 142300025 17500103 12410011 55000106 11180001 190001010 153200016 207100015 9500013 188501012 28201020 16350107 12500113 11670104 125000010 12500107 17640117 7590116 8650002 335601112 16810118 4790103 12250011 10000014 150000011 17300012 17300003 15000003 168100130 19180116 11500009 90000030 90001028 7200005 14960001 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