MA684 Final ProjectSpring 2019This is an individual project—please do your own work. Some discussion with other students aroundcomputer work for the project is permitted, but you should formulate and perform the analyses onyour own, and write up your results on your own. (A good rule of thumb: only write answers whenyou are completely alone.) Questions about the content of the project or programming issues can bedirected to the instructor or TF. This project makes up a substantial proportion of your grade, so please provide an organized,professional, and well-edited write-up. Please write up your results in paragraph form—do not simplyannotate computer output. This is a statistics class, so please present appropriate statistical detail—identify the statistical methods that you use, explain how you reach your conclusions, report teststatistics along with P-values to make it clear what information is being reported. Please report yourresults in the context of the problem. If your write-up is complete, we should not need to refer to yourcomputer output, but please submit your computer output as an appendix with the project. (Thesyntax will be submitted on BlackBoard, while the report will be turned in on paper at the final exam.) You will be graded on (1) running the analyses correctly (worth 50%), (2) providing correct syntax(all analyses must be run using syntax, but for small calculations such as p-values or effect sizes; worth10%), and (3) your professional write-up of the solutions (worth 40%). The final write-up should bewritten as though it were a final report being provided to a research client…ideally, your write-up willbe complete enough so we won’t have to refer to the computer output. The document should followthe 5 C’s of communication: clear, concise, correct, cogent & comprehensive. The project is due Thursday May 9 (3:00pm). Projects should be submitted at start of final exam(printout report) and submit syntax on BlackBoard. If you encounter any problems, please email acopy of your final project to both the instructor (harbaugh@bu.edu) and the TF and please include your name on the project.You are running analyses for a moderately-sized company located in the Pacific Northwest. (Note, thisis a hypothetical scenario with hypothetical data.) The company states that it values diversity andequal opportunities for all of its employees, regardless of gender, race or any other categorization.They have hired you to help them examine their employee evaluation and promotion process.The employees in this data set were with the company for at least two years (one full year beyond theprobationary period), and this subset of the data includes only employees classified at the supervisorylevel or below (no managers or executives) at the time of the most recent evaluation cycle. The dataon each employee is the following: emp_ID: a unique code for each employee in the data set jobrating: a score assigned by the employee’s direct supervisor on a 0–100 scale. salary: 12-month adjusted FT salary, in USD gender_F: dummy variable for gender identification, coded 0 for male and 1 for female MA684留学生作业代写、Java/Python实验作业代写、代做c/c++编程语言作业、代写programming作业 race: categorical variable, coded 0 for white, 1 for Asian, and 2 for other promote: dummy variable indicating if employee was promoted within the past 11-monthsThere are no missing data in this final version of the data set.In addition to these concrete variables, the employees also completed the “Personality Questionnaire”.Items on this self-rated instrument asked how well the following word/phrase describes the employeeon a 1 (not at all like me) to 5 (very much like me) scale: do a thorough job reliable perseveres original imaginative shy reserved quiet sticks to a plan curious inventiveThe ultimate goal of the study is to determine what sort of people get good job ratings and getpromoted. In particular, the clients are interested in the association between personality type and jobratings. However, they also want to examine any other possible relationships that may or may notrequire attention.For this analysis, there are a few necessary conversions/transformations for the data. First, be surethat you have coded the race variable as a categorical (factor) variable, or have created the necessarydummy variables. Please use “white” as the reference group. For gender, no changes are necessary,but please use “male” as the reference group. As is common with variables such as salary, it is betterto work with the log(salary) instead of the untransformed value. Create a new variable, and use thislog_salary variable in your analyses. Lastly, convert the questionnaire data into a smaller set ofsubscales, as indicated next.First, it is necessary to summarize the data from the personality questionnaire. Conduct a principalcomponents analysis with promax (non-orthogonal) rotation to determine the number of factors.Then conduct an exploratory factor analysis on the 11 personality variables to develop summarymeasures of personality. (In your final write-up, be sure to indicate ?How many summary measuresare needed to describe personalities? ?How well do these summary measures capture the informationfrom the 11 personality variables? ?What is measured by these summary measures? and for all of youranswers, how did you reach this conclusion?)Examine all possible bivariate relationships among the data: examine multicollinearity (between pairsof independent variables) and possible confounding (between indep. vars. and the dependentvariable). Of particular importance are questions regarding whether gender and/or race are related tothe dependent variables (with and without controlling for the possible confounding variables). Youprobably will want to explore models that examine the effect of gender after controlling for othervariables.Based on your decisions to generate scales for the personality measures, run a regression analysis topredict job rating from all of the available information. Next, run a logistic regression analysis topredict promotions from all of the available information (including job rating). Construct the bestmodels (and justify the choice for those models). Assess model fit.For all dichotomous variable analyses, se sure to report and interpret relevant odds-ratios.转自：http://www.7daixie.com/2019050812414278.html
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