Two variables (in the dataset) to analyze based on your research question: a) One independent variable (IV) and an explanation of what kind of scale/variable it is (e.g., nominal/groups, int
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Get it written →I attached the data set. Follow the data set and do the below questions. Need 2 pages.
1. One research question
2. Two variables (in the dataset) to analyze based on your research question:
a) One independent variable (IV) and an explanation of what kind of scale/variable it is (e.g., nominal/groups, interval, ordinal)
b) At least one dependent variable (DV) an explanation of what kind of scale/variable it is (e.g., nominal/groups, interval, ordinal)
3. At least one hypothesis that you will test using data analysis, hypothesizing the relationship between the IV and DV. Note that your hypothesis and variables should allow you to utilize a hypothesis test we have learned/are learning about in class (e.g., T-test, ANOVA, correlation between two continuous variables
| Age | Attrition | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | Over18 | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager |
| 41 | Yes | Travel_Rarely | 1102 | Sales | 1 | 2 | Life Sciences | 1 | 1 | 2 | Female | 94 | 3 | 2 | Sales Executive | 4 | Single | 5993 | 19479 | 8 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 49 | No | Travel_Frequently | 279 | Research & Development | 8 | 1 | Life Sciences | 1 | 2 | 3 | Male | 61 | 2 | 2 | Research Scientist | 2 | Married | 5130 | 24907 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 37 | Yes | Travel_Rarely | 1373 | Research & Development | 2 | 2 | Other | 1 | 4 | 4 | Male | 92 | 2 | 1 | Laboratory Technician | 3 | Single | 2090 | 2396 | 6 | Y | Yes | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 33 | No | Travel_Frequently | 1392 | Research & Development | 3 | 4 | Life Sciences | 1 | 5 | 4 | Female | 56 | 3 | 1 | Research Scientist | 3 | Married | 2909 | 23159 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 27 | No | Travel_Rarely | 591 | Research & Development | 2 | 1 | Medical | 1 | 7 | 1 | Male | 40 | 3 | 1 | Laboratory Technician | 2 | Married | 3468 | 16632 | 9 | Y | No | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| 32 | No | Travel_Frequently | 1005 | Research & Development | 2 | 2 | Life Sciences | 1 | 8 | 4 | Male | 79 | 3 | 1 | Laboratory Technician | 4 | Single | 3068 | 11864 | 0 | Y | No | 13 | 3 | 3 | 80 | 0 | 8 | 2 | 2 | 7 | 7 | 3 | 6 |
| 59 | No | Travel_Rarely | 1324 | Research & Development | 3 | 3 | Medical | 1 | 10 | 3 | Female | 81 | 4 | 1 | Laboratory Technician | 1 | Married | 2670 | 9964 | 4 | Y | Yes | 20 | 4 | 1 | 80 | 3 | 12 | 3 | 2 | 1 | 0 | 0 | 0 |
| 30 | No | Travel_Rarely | 1358 | Research & Development | 24 | 1 | Life Sciences | 1 | 11 | 4 | Male | 67 | 3 | 1 | Laboratory Technician | 3 | Divorced | 2693 | 13335 | 1 | Y | No | 22 | 4 | 2 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Frequently | 216 | Research & Development | 23 | 3 | Life Sciences | 1 | 12 | 4 | Male | 44 | 2 | 3 | Manufacturing Director | 3 | Single | 9526 | 8787 | 0 | Y | No | 21 | 4 | 2 | 80 | 0 | 10 | 2 | 3 | 9 | 7 | 1 | 8 |
| 36 | No | Travel_Rarely | 1299 | Research & Development | 27 | 3 | Medical | 1 | 13 | 3 | Male | 94 | 3 | 2 | Healthcare Representative | 3 | Married | 5237 | 16577 | 6 | Y | No | 13 | 3 | 2 | 80 | 2 | 17 | 3 | 2 | 7 | 7 | 7 | 7 |
| 35 | No | Travel_Rarely | 809 | Research & Development | 16 | 3 | Medical | 1 | 14 | 1 | Male | 84 | 4 | 1 | Laboratory Technician | 2 | Married | 2426 | 16479 | 0 | Y | No | 13 | 3 | 3 | 80 | 1 | 6 | 5 | 3 | 5 | 4 | 0 | 3 |
| 29 | No | Travel_Rarely | 153 | Research & Development | 15 | 2 | Life Sciences | 1 | 15 | 4 | Female | 49 | 2 | 2 | Laboratory Technician | 3 | Single | 4193 | 12682 | 0 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 9 | 5 | 0 | 8 |
| 31 | No | Travel_Rarely | 670 | Research & Development | 26 | 1 | Life Sciences | 1 | 16 | 1 | Male | 31 | 3 | 1 | Research Scientist | 3 | Divorced | 2911 | 15170 | 1 | Y | No | 17 | 3 | 4 | 80 | 1 | 5 | 1 | 2 | 5 | 2 | 4 | 3 |
| 34 | No | Travel_Rarely | 1346 | Research & Development | 19 | 2 | Medical | 1 | 18 | 2 | Male | 93 | 3 | 1 | Laboratory Technician | 4 | Divorced | 2661 | 8758 | 0 | Y | No | 11 | 3 | 3 | 80 | 1 | 3 | 2 | 3 | 2 | 2 | 1 | 2 |
| 28 | Yes | Travel_Rarely | 103 | Research & Development | 24 | 3 | Life Sciences | 1 | 19 | 3 | Male | 50 | 2 | 1 | Laboratory Technician | 3 | Single | 2028 | 12947 | 5 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 6 | 4 | 3 | 4 | 2 | 0 | 3 |
| 29 | No | Travel_Rarely | 1389 | Research & Development | 21 | 4 | Life Sciences | 1 | 20 | 2 | Female | 51 | 4 | 3 | Manufacturing Director | 1 | Divorced | 9980 | 10195 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 10 | 1 | 3 | 10 | 9 | 8 | 8 |
| 32 | No | Travel_Rarely | 334 | Research & Development | 5 | 2 | Life Sciences | 1 | 21 | 1 | Male | 80 | 4 | 1 | Research Scientist | 2 | Divorced | 3298 | 15053 | 0 | Y | Yes | 12 | 3 | 4 | 80 | 2 | 7 | 5 | 2 | 6 | 2 | 0 | 5 |
| 22 | No | Non-Travel | 1123 | Research & Development | 16 | 2 | Medical | 1 | 22 | 4 | Male | 96 | 4 | 1 | Laboratory Technician | 4 | Divorced | 2935 | 7324 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 2 | 1 | 2 | 2 | 1 | 0 | 0 | 0 |
| 53 | No | Travel_Rarely | 1219 | Sales | 2 | 4 | Life Sciences | 1 | 23 | 1 | Female | 78 | 2 | 4 | Manager | 4 | Married | 15427 | 22021 | 2 | Y | No | 16 | 3 | 3 | 80 | 0 | 31 | 3 | 3 | 25 | 8 | 3 | 7 |
| 38 | No | Travel_Rarely | 371 | Research & Development | 2 | 3 | Life Sciences | 1 | 24 | 4 | Male | 45 | 3 | 1 | Research Scientist | 4 | Single | 3944 | 4306 | 5 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 3 | 2 | 1 | 2 |
| 24 | No | Non-Travel | 673 | Research & Development | 11 | 2 | Other | 1 | 26 | 1 | Female | 96 | 4 | 2 | Manufacturing Director | 3 | Divorced | 4011 | 8232 | 0 | Y | No | 18 | 3 | 4 | 80 | 1 | 5 | 5 | 2 | 4 | 2 | 1 | 3 |
| 36 | Yes | Travel_Rarely | 1218 | Sales | 9 | 4 | Life Sciences | 1 | 27 | 3 | Male | 82 | 2 | 1 | Sales Representative | 1 | Single | 3407 | 6986 | 7 | Y | No | 23 | 4 | 2 | 80 | 0 | 10 | 4 | 3 | 5 | 3 | 0 | 3 |
| 34 | No | Travel_Rarely | 419 | Research & Development | 7 | 4 | Life Sciences | 1 | 28 | 1 | Female | 53 | 3 | 3 | Research Director | 2 | Single | 11994 | 21293 | 0 | Y | No | 11 | 3 | 3 | 80 | 0 | 13 | 4 | 3 | 12 | 6 | 2 | 11 |
| 21 | No | Travel_Rarely | 391 | Research & Development | 15 | 2 | Life Sciences | 1 | 30 | 3 | Male | 96 | 3 | 1 | Research Scientist | 4 | Single | 1232 | 19281 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 0 | 6 | 3 | 0 | 0 | 0 | 0 |
| 34 | Yes | Travel_Rarely | 699 | Research & Development | 6 | 1 | Medical | 1 | 31 | 2 | Male | 83 | 3 | 1 | Research Scientist | 1 | Single | 2960 | 17102 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 8 | 2 | 3 | 4 | 2 | 1 | 3 |
| 53 | No | Travel_Rarely | 1282 | Research & Development | 5 | 3 | Other | 1 | 32 | 3 | Female | 58 | 3 | 5 | Manager | 3 | Divorced | 19094 | 10735 | 4 | Y | No | 11 | 3 | 4 | 80 | 1 | 26 | 3 | 2 | 14 | 13 | 4 | 8 |
| 32 | Yes | Travel_Frequently | 1125 | Research & Development | 16 | 1 | Life Sciences | 1 | 33 | 2 | Female | 72 | 1 | 1 | Research Scientist | 1 | Single | 3919 | 4681 | 1 | Y | Yes | 22 | 4 | 2 | 80 | 0 | 10 | 5 | 3 | 10 | 2 | 6 | 7 |
| 42 | No | Travel_Rarely | 691 | Sales | 8 | 4 | Marketing | 1 | 35 | 3 | Male | 48 | 3 | 2 | Sales Executive | 2 | Married | 6825 | 21173 | 0 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 9 | 7 | 4 | 2 |
| 44 | No | Travel_Rarely | 477 | Research & Development | 7 | 4 | Medical | 1 | 36<
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