Your people ARE your most important asset! They determine the customer experience. If it's good you win, if it's poor you lose. It's really that simple, says James Kerr, a human resource consultant. Therefore more and more effort is going into trying to select those that would be the best fit for the organization. Investments are made in training employees that sometimes take a long time and cost a lot of money. Sometimes excessive training might be related to changing environments which might have a negative impact on employee satisfaction. As a result, there is more and more effort being placed on retaining employees once the investment is made. Furthermore, more and more companies are searching for ways to predict if an employee is happy or frustrated so that they might be able to take action before the employee decides to quit. In this case we will be looking at an actual data set of a manufacturing company.

There are a total of 187 observations with a total of 40 variables. Of these 40 variables, two are indicator variables that should not be used in any quantitative analysis that you might perform. (Name and ID number). There are 12 binary variables: MarriedID, GenderID, EmplActiveID, PerfScoreID>3, FromDiversityJobFairID, PositionID, AgeID, HispanicLatino, Minority, FrictionNoted, AbsenceID, and PromotionDenial. We would like to use these binary variables to run a series of hypothesis test to determine if any of them have an impact on one or more of our continuous Y variables. Data set: Total Sample Size: 187 Observations 1. Employee Name : Employees full name (Not to be included in any analysis) 2. EmpID : Employee ID is unique to each employee (Not to be included in any analysis) 3. MarriedID : Binary Is the person married (1 or 0 for yes or no) 4. MaritalStatusID : Marital status code that matches the text field MaritalDesc 0-single, 1 married, 2-divorced, 3-seperated) 5. GenderID: Binary (0-Female, 1-Male) 6. EmplActiveID: Binary 1-active, 0-terminated 7. DeptID : Binary for this data set Department ID code that matches the department the employee works in 8. PerfScoreID is 3 or more. Binary (Yes or No) 9. FromDiversityJobFairID : Binary Was the employee recruited from a Diversity job fair? 1 or 0 for yes or no 10. Salary (Annually) 11. PositionID : Binary An integer indicating the persons position 12. Position : The text name/title of the position the person has 13. State : The state that the person lives in 14. Zip : The zip code for the employee 15. DOB : Date of Birth for the employee 16. Age rounded to years 17. AgeID: older than 39 Binary (0 if no, 1 if yes) 18. MaritalDesc : The marital status of the person (divorced, single, widowed, separated, etc) 19. CitizenDesc : Label for whether the person is a Citizen or Eligible NonCitizen 20. HispanicLatino : Binary Yes or No field for whether the employee is Hispanic/Latino 21. Minority Binary yes or no 22. DateofHire : Date the person was hired 23. DateofTermination : Date the person was terminated, only populated if terminated 24. TermReason : A text reason / description for why the person was terminated 25. EmploymentStatus : A description/category of the persons employment status. Anyone currently working full time = Active 26. Department : Name of the department that the person works in 27. ManagerName : The name of the persons immediate manager 28. ManagerID : A unique identifier for each manager. 29. RecruitmentSource : The name of the recruitment source where the employee was recruited from 30. PerformanceScore : Performance Score text/category (Fully Meets, Partially Meets, PIP, Exceeds) 31. PerfScoreID : Performance Score code that matches the employees most recent performance score 32. EngagementSurvey : Results from the last engagement survey, managed by our external partner 33. EmpSatisfaction : A basic satisfaction score between 1 and 5, as reported on a recent employee satisfaction survey 34. Friction Noted Binary (0-No, 1-Yes) Friction noted with manager or coworker in the last six months. 35. SpecialProjectsCount : The number of special projects that the employee worked on during the last 6 months 36. LastPerformanceReviewDate : The most recent date of the persons last performance review. 37. DaysLateLast30 : The number of times that the employee was late to work during the last 30 days 38. Absences; The number of absences in the last 12 months 39. AbsenceID Binary Days Absent 10 or more. 40. Promotion Denial: Binary (0-No 1-Yes) Denial in the last 6 months. question1 . A. Calculate the 90% confidence interval for the EngagementSurvey variable of everyone in the sample. Interpret this interval.