[Solved] w6

Assignment Prompt & Instructions

[>Ecology homework help](https://www.sweetstudy.com/fields/biology-ecology)

– a month ago

**files (2)**

– ## Tasks6.docx

– MFGemployeeabsenteeism-3Task6.xlsx

**## Tasks6.docx**

✅ Tasks

1.
**Create a New Variable – AbsentRate**

· Open the dataset
[MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860?wrap=1) [Download MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860/download?download_frd=1)

· Create a new column named
**AbsentRate**.

· Formula:

(Assume each employee works 40 hours per week × 52 weeks = 2080 total hours).

2.
**Descriptive Analysis – Annual Absenteeism**

· Calculate the
**overall average AbsentRate** for the organization.

· Compare absenteeism
**by gender** (e.g., use PivotTables and bar charts).

· Report the annual rate and whether differences exist by gender.

3.
**Predictive Analysis – Regression Model**

· First, run
**correlation analysis** (Data → Data Analysis → Correlation) to identify potential predictors of AbsentRate.

· Then, run a
**regression** (Data → Data Analysis → Regression) with AbsentRate as the dependent variable.

· Report:

· Which predictors are statistically significant (p < .05).

· R² or Adjusted R² values (how well the model explains absenteeism).

· The regression equation.

4.
**Conclusions & Recommendations**

· Summarize the key findings:

· Annual absenteeism rate.

· Differences by gender.

· Predictive insights (important variables).

· Provide practical recommendations to the executives on how the company should manage absenteeism.

**MFGemployeeabsenteeism-3Task6.xlsx**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/mfgemployeeabsenteeism-3task6-xlsx)

**MFGemployeeabsenteeism-3Task6.xlsx**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/mfgemployeeabsenteeism-3task6-xlsx)

**## Tasks6.docx**

✅ Tasks

1.
**Create a New Variable – AbsentRate**

· Open the dataset
[MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860?wrap=1) [Download MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860/download?download_frd=1)

· Create a new column named
**AbsentRate**.

· Formula:

(Assume each employee works 40 hours per week × 52 weeks = 2080 total hours).

2.
**Descriptive Analysis – Annual Absenteeism**

· Calculate the
**overall average AbsentRate** for the organization.

· Compare absenteeism
**by gender** (e.g., use PivotTables and bar charts).

· Report the annual rate and whether differences exist by gender.

3.
**Predictive Analysis – Regression Model**

· First, run
**correlation analysis** (Data → Data Analysis → Correlation) to identify potential predictors of AbsentRate.

· Then, run a
**regression** (Data → Data Analysis → Regression) with AbsentRate as the dependent variable.

· Report:

· Which predictors are statistically significant (p < .05).

· R² or Adjusted R² values (how well the model explains absenteeism).

· The regression equation.

4.
**Conclusions & Recommendations**

· Summarize the key findings:

· Annual absenteeism rate.

· Differences by gender.

· Predictive insights (important variables).

· Provide practical recommendations to the executives on how the company should manage absenteeism.

**MFGemployeeabsenteeism-3Task6.xlsx**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/mfgemployeeabsenteeism-3task6-xlsx)

**## Tasks6.docx**

✅ Tasks

1.
**Create a New Variable – AbsentRate**

· Open the dataset
[MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860?wrap=1) [Download MFG employee absenteeism-3.xlsx](https://henderson.instructure.com/courses/24639/files/2662860/download?download_frd=1)

· Create a new column named
**AbsentRate**.

· Formula:

(Assume each employee works 40 hours per week × 52 weeks = 2080 total hours).

2.
**Descriptive Analysis – Annual Absenteeism**

· Calculate the
**overall average AbsentRate** for the organization.

· Compare absenteeism
**by gender** (e.g., use PivotTables and bar charts).

· Report the annual rate and whether differences exist by gender.

3.
**Predictive Analysis – Regression Model**

· First, run
**correlation analysis** (Data → Data Analysis → Correlation) to identify potential predictors of AbsentRate.

· Then, run a
**regression** (Data → Data Analysis → Regression) with AbsentRate as the dependent variable.

· Report:

· Which predictors are statistically significant (p < .05).

· R² or Adjusted R² values (how well the model explains absenteeism).

· The regression equation.

4.
**Conclusions & Recommendations**

· Summarize the key findings:

· Annual absenteeism rate.

· Differences by gender.

· Predictive insights (important variables).

· Provide practical recommendations to the executives on how the company should manage absenteeism.

**MFGemployeeabsenteeism-3Task6.xlsx**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/mfgemployeeabsenteeism-3task6-xlsx)