Question

This document contains only a list of the questions for homework 10. Please submit your responses through the assignment in Learning Suite. Imagine that your company is interested in learning what factors influence the productivity of a group of employees during their first year working at the company. Simulated data on employee productivity are contained in the "HW10Data.csv" file which accompanies these questions. In the file, the variable "Productivity" contains a measure of an employee’s productivity during a given week; the variable "Tenure_Weeks" reports employee tenure in weeks; the variable "Unit" reports the department in which an employee works; the variable "Demand" contains a measure of fluctuations in demand for product line on which an employee works; and the variable "OnboardingCompletion" reports the percentage of the company’s onboarding program that a given employee completed. Please conduct a regression analysis of this data set and answer the following questions based on your analysis.

Initially, please create a regression model using "Productivity" as the dependent variable and all other variables (except for "EmployeeId") as independent variables. For the time being, please assume that relationships among all the variables are linear and take no steps to "linearize" them. Which of the independent variables has at least a 95% probability of being meaningfully related to "Productivity"?

Answer 1

When determining which factors influence employee productivity, conduct a regression analysis using Productivity as the dependent variable, and then identify independent variables with P-values less than 0.05 to establish a significant relationship.

Step-by-step explanation:

To answer which independent variables are meaningfully related to Productivity, a regression analysis must be conducted with Productivity as the dependent variable, and Tenure_Weeks, Unit, Demand, and OnboardingCompletion as independent variables.

In conducting this analysis, we look for P-values below 0.05, which indicate a 95% probability the relationship is not due to random chance. This regression also involves calculating the least-squares line, typically represented as
`\(\hat{y} = a + bx\)`, where
`\(\hat{y}\)` is the predicted value of the dependent variable, a is the y-intercept, and b is the slope of the line.

When organizing data, as in the example of productivity by country, a table format can be helpful for clarity. In addition to regression, other analyses such as finding the correlation coefficient will help in understanding the strength and significance of the relationship between variables.