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OPTION A: Gender and Racial differences in payments in federal jobs Mr. Cartwright is the director of the Office of Personnel and Management (OPM)....

I've worked on this project quite a bit but I just don't understand it. I'm willing to negotiate compensation for doing this assignment and I would really appreciate it. It is a regression analysis using different variables and the relevant files are attached below. 

OPTION A: Gender and Racial differences in payments in federal jobs Mr. Cartwright is the director of the Office of Personnel and Management (OPM). One employee recently complained to him that even in Federal jobs there exist unexplained salary differences between male and female employees and employees of different racial background. Mr. Cartwright is of the opinion that educational and other ‘quality’ measures are reasons of such differences and there is no gender or race bias in the federal salaries. He has asked you, the brilliant analysts from MGS 3100 to create a regression model to prove or disprove his claim. He has supplied you with the dataset salary_federal.xls which contains the salary and other information about some randomly sampled federal employees whose names are withheld for reasons of anonymity and to protect their privacy. Your task is to create a regression model that contains at least FIVE (more would be better, but five is the minimum necessary) explanatory variables (that explain/predict) salary of these employees. From this regression model you have to conclude whether you have found any evidence to support the claim of gender and 2 racial discrimination in federal payment structure or not. Description of the dataset is provided at the end of this document. What you need to do in your project as part of the data analysis (whether you choose Option A or Option B) 1. Show descriptive statistics of all the variables. [to get some feel for the data]. Descriptive statistics are average or mean, standard deviation, maximum, minimum, etc. 3 2. Show relationships of each independent variable individually with the dependent variable using scatter plots. Remember to correctly label the horizontal and vertical axes in each diagram. [this is to get some initial feeling about which variables are more related to the dependent variables, and look for possible outliers or influence points] (In Excel: Insert – Charts – Scatter) 3. Perform regression analysis to show overall model for predicting the value of the dependent variable. (You might need to activate the Analysis toolpack Excel plug-in/addin in your Excel. Once you have done that regression can be run by Data – Analysis – Regression). But as we discussed in class, the first model that you estimate is most likely not be your final model. You will need to drop (sometimes add as well) bad variables from the model and reestimate the model again. This is a crucial step. Do not ask me how many times you should reestimate or how many models should be there – there is no answer to that. What I want to see indication of the fact that you have understood the idea of how regression analysis is done through trial and error of including and dropping of variables. So start with a handful (much more than 5) of variables that can be expected to affect your dependent variable. Then drop the ones that does not seem good after you do your initial regression or regressions. This is an iterative
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process and will take both time and patience. We can talk more about this step during office hour. 4. Interpret the results and write a report using those interpretations that conforms to the sample memo format. Submittals: Report - Single spaced one or two pages Word document (do not embed in Excel) that conforms to the sample memo format and contains: Introduction should contain 1. Why you are interested in the topic: the background of your project 2. What you are trying to predict – your dependent variable. 3. Why you choose certain independent variables for your project. Why do you think those independent or explanatory variables are going to affect the dependent of explained variable? [ Logical explanation is required. ] 4. How you collected the data (e.g., survey or from Web or from some book) [ You should clearly write the source of the data. ] Analysis [do not insert any graph or diagram] 4 5. Findings from descriptive statistics and scatter plot [ It does not have to be thorough; write what variables are expected to have relationship with the dependent variable. ] 6. List of insignificant independent variables in a full model (i.e., a model with all the independent variables) 7. Order of the dropped independent variables in a subsequent regression analyses with reasoning of such order 8. Equation of the final model (i.e., a model with only significant independent variable(s)) 9. Performance of the final model. i.e., how good your model is? Look at your F-significance number and R-squared value for answering this. [ Low R-squared does not mean low grade. ] 10. Findings from the final model. i.e., interpretation of the coefficients of the independent variables [ Make sure that all the coefficients make sense. If it does not, explain further how such odd coefficient can be justified ] Excel file Your excel file should have raw data and outputs of all the regression analyses you did. Each worksheet should be clearly named "raw data," "scatter plot," "regression 1, (Full Model)" "regression 2," and so on… finishing with “Final Regression”. But note that I am not saying you will run three regressions only (Full Model, Regression 2, and final model). You may need more depending on the iterative process that I discussed in point 3 above. [Example] Your report should be written clearly about what you want to do in the project, what you have found, and how you have found. Your report should be easy to read and understand without referring to your Excel file.
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sal grade patco age male vet yos edyrs promo supmgr race 55250 12 professional 49 male yes 23 16 no yes white 74765 14 professional 57 male yes 19 20 yes no white 95469 15 professional 55 male no 17 18 no yes white 51235 12 professional 61 male no 38 16 yes white 55203 13 professional 36 male no 6 20 no no white 62102 13 professional 37 male no 5 20 no no white 85249 15 administrative 50 male yes 24 16 no yes white 39719 11 administrative 48 female no 24 14 no yes black 31262 9 professional female no 3 16 no no white 49850 12 professional 44 male yes 16 18 no no white 34662 11 professional 28 female no 2 18 yes no white 39009 11 professional 32 female no 9 14 no no white 56605 13 professional 42 male no 13 20 no no white 63399 14 professional 47 male yes 15 18 yes white 76814 14 professional 55 male no 32 18 no yes white 46103 11 professional 58 male yes 19 16 no no white 65619 13 administrative 62 male yes 21 18 no yes white 34662 11 administrative 28 female no 5 16 no white 116529 16 administrative 64 male yes 17 18 no yes white 47013 12 professional 47 male yes 9 16 no no white 45064 12 professional 28 female no 3 16 no no white 49002 12 administrative 50 male no 18 16 no no white 64928 13 administrative 56 female no 34 16 no no white 59756 13 professional 41 male no 3 16 no no white 44802 12 administrative 30 female no 8 16 no no white 45063 11 professional 63 male yes 31 18 no no white 68111 14 professional 45 male no 11 18 no yes white 65026 14 administrative 48 male yes 24 16 no yes white 34375 9 professional 48 female no 13 16 no no black 67880 14 professional 61 male yes 26 18 yes white 66894 14 professional 44 male no 11 18 no no white 73391 14 professional 49 male no 24 16 no no white 74054 15 administrative 46 male yes 18 15 no no white 30557 9 professional 62 male yes 10 18 no no white 28622 7 administrative 41 female no 15 12 no no white 65235 14 administrative 29 male no 7 13 no no white 56101 13 administrative 61 female no 28 13 yes white 41543 12 administrative 37 male no 4 16 no no white 74741 14 administrative 56 male no 28 18 no yes white 47081 12 administrative 37 male no 7 16 no no white 38734 11 professional 45 male no 4 16 no no white 52619 12 administrative 50 male yes 18 16 no no white 54941 13 professional 47 male no 12 18 no no white 36313 11 administrative 39 female no 6 16 no no white 36085 11 administrative 35 female no 10 12 no no black 42928 12 administrative 42 female no 12 18 yes no white 48304 12 administrative 34 male no 6 15 no no white 36123 9 professional 25 male no 2 16 no white 61479 13 administrative 56 male no 18 16 no yes white 35818 11 professional 39 female no 10 16 no no white 51612 13 professional 29 female no 4 18 no no black 68294 14 administrative 56 female no 38 12 yes white 30557 9 administrative 31 male no 9 12 no black 63263 13 professional 41 male no 17 16 no yes white 68108 14 administrative 30 male no 10 12 no no white 76733 14 administrative 47 female no 19 18 yes no white 51801 12 administrative 55 male yes 28 16 no no white 48466 12 professional 52 female no 19 12 no no white 32466 9 administrative 52 male yes 19 12 no no white 64926 14 professional 34 male no 12 16 no no white 54340 13 professional 43 female yes 6 20 no black 75532 15 administrative 41 male no 17 18 no yes white 111839 16 professional 47 male no 25 18 no yes white 57280 13 professional 44 male no 16 16 no yes white 54340 13 administrative 45 male no 23 16 no yes white 41596 11 administrative 46 female no 25 14 no no white 44972 12 administrative 39 female no 19 12 yes no white 83926 15 administrative 37 male yes 15 12 no yes white 45063 11 administrative 50 male yes 27 12 no white 58026 12 professional 45 male no 3 18 no no white 48356 12 professional 31 male no 6 16 no no white 47873 12 professional 36 female no 12 16 no no white 58270 13 professional 40 male no 3 16 no no white 60378 13 administrative 47 female no 21 13 no yes black 50541 13 professional 33 female no 3 20 no no white 51612 13 administrative 44 female no 23 16 no no black 75289 14 professional 52 male no 27 18 no no white 79509 14 professional 49 male no 29 16 no yes white 50774 12 administrative 43 female no 20 18 no no white 56576 12 professional 48 male no 26 16 no no white 67135 14 professional 42 male no 7 18 no yes white 71352 14 professional 48 male no 21 16 no yes white 42366 11 administrative 45 male yes 11 14 yes no white 28101 7 administrative 50 female no 19 12 no no white 62958 14 professional 32 female no 10 16 yes yes white 41596 11 administrative 47 female no 17 16 no no white 47013 12 administrative 54 male yes 13 18 no no white 75894 14 administrative 59 male yes 32 18 no yes white 49345 12 professional 46 female no 26 12 no no white 45116 12 professional 31 male no 10 16 no no white 76733 14 professional 53 female no 28 18 no yes black 51419 12 administrative 38 female no 13 14 no no white 54941 13 administrative 46 female no 28 13 no no white 34755 9 administrative 54 female no 22 12 no no white 51612 13 professional 31 male no 5 16 yes no white
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68865 14 administrative 46 male yes 20 18 no yes white 83644 15 professional 48 male no 19 18 yes yes black 39285 11 professional 46 male yes 20 16 no no white 74491 15 administrative 48 female no 20 14 no yes white 52693 13 professional 30 male no 7 16 no no white 59934 13 professional 49 female no 29 14 no no black 49850 12 administrative 48 male yes 13 12 no yes white 36013 9 administrative 59 female yes 26 16 no black 38821 11 administrative 34 male no 15 12 yes no black 51612 13 administrative 48 female no 15 13 no yes white 48466 12 professional 44 male no 17 18 no no white 49850 12 administrative 48 male yes 23 16 no yes white 41470 11 professional 42 male no 7 16 no no white 72783 14 professional 44 male yes 16 18 no no white 64928 13 administrative 47 male yes 17 18 no yes white 47081 12 professional 39 male no 16 16 no no white 28882 7 administrative 40 female no 22 12 no no white 55325 13 professional 37 female no 18 16 no no white 42003 12 administrative 29 female no 5 16 no no white 90252 15 administrative 60 male yes 35 16 no yes white 40440 11 administrative 47 male yes 13 13 no no white 54941 13 administrative 52 female no 34 12 no yes white 59517 13 administrative 43 female no 14 16 no yes white 30895 9 administrative 28 female no 9 12 no no white 54941 13 professional 45 male no 17 16 no no white 61606 13 professional 41 male no 10 18 no yes white 47168 12 administrative 45 male yes 12 15 no no white 46202 12 professional 33 female no 11 16 no no white 53201 12 administrative 44 female no 22 16 no no white 71816 14 professional 44 male no 11 20 yes yes white 44802 12 professional 50 female no 4 18 no no white 46202 12 administrative 30 female no 11 12 yes no white 54601 12 administrative 46 female no 28 12 no no white 82159 15 administrative 46 male no 22 16 no yes white 57208 13 administrative 39 female no 15 18 no no white 36214 11 professional 30 female no 2 16 yes no white 32825 9 administrative 43 female no 23 12 no no white 64928 13 professional 49 male no 27 16 no no white 26834 7 administrative 48 male yes 3 16 no no white 41543 12 administrative 33 male no 5 12 yes no white 28648 9 administrative 33 male no 2 16 no no white 51612 13 administrative 36 female no 12 16 no yes white 40349 11 professional 31 male no 13 16 yes no white 47602 12 professional 32 female no 13 12 no no white 58270 13 administrative 38 male no 15 16 no no white 53201 12 administrative 45 female no 27 16 no no black 50401 12 administrative 45 male yes 16 16 no no white 39719 11 administrative 43 female no 24 12 no no black 43402 12 administrative 41 female no 21 12 no no black 43348 12 administrative 33 male no 10 15 no no white 64926 14 administrative 42 male yes 14 14 no no white 61172 14 administrative 40 female no 8 18 yes white 46202 12 professional 33 female no 9 16 yes no white 106106 16 administrative 50 female no 25 18 no yes white 64928 13 professional 32 female no 1 16 yes no white 28964 9 administrative 27 female no 1 18 yes no white 45063 11 administrative 44 female no 22 14 no no white 67983 14 administrative 45 male no 21 18 no yes white 42003 12 administrative 44 female no 20 14 no no black 59934 13 administrative 51 male no 24 16 no no white 56605 13 professional 45 male no 23 16 no no white 46202 12 professional 32 female no 6 16 yes no white 39013 9 administrative 52 female no 30 14 no no black 44802 12 administrative 38 male no 3 13 no no white 49850 12 administrative 38 male yes 16 16 no yes white 83313 15 professional 48 male no 26 16 no yes white 64928 13 administrative 47 female no 26 16 no yes white 23883 6 administrative 37 female no 10 12 no no white 37382 11 administrative 40 female no 15 12 no no white 54340 13 administrative 36 female no 16 13 no yes black 111839 16 administrative 48 male yes 23 16 no yes white 39285 11 professional 35 male no 12 16 no no white 31512 9 administrative 32 female no 10 16 no no white 54941 13 administrative 34 male no 12 12 no no white 29602 9 administrative 25 female no 3 16 no white 43402 12 administrative 28 male no 3 14 no no white 38551 11 administrative 53 female no 34 14 no no white 47873 12 professional 41 female no 9 18 no no white 43706 12 administrative 30 male no 6 16 no no white 61599 13 administrative 50 male no 24 15 no no white 49850 12 administrative 47 male no 28 12 no no white 45697 12 administrative 46 male yes 22 16 no no black 47873 12 professional 44 female no 22 12 no yes white 56089 13 professional 34 male no 11 16 no no white 38551 11 administrative 36 female no 18 12 no no white 111839 16 administrative 58 male no 36 16 yes white 60672 13 administrative 47 male yes 26 16 no yes black 64845 14 professional 39 female no 11 16 no no white 38129 11 administrative 33 male no 14 12 no no white 36973 11 administrative 54 female no 17 12 no no white 66894 14 administrative 57 female no 32 16 no yes white 41672 11 professional 45 male no 23 12 no yes black 60990 14 administrative 37 female no 16 12 no no white 56605 13 administrative 67 male yes 11 16 no white 51329 12 professional 39 female no 17 16 no no black 81528 15 professional 40 male no 16 18 no no white
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