Objective: Analyze the factors influencing the duration of employment (EmploymentDuration) among participants using survival analysis techniques. Assumption: Assume that the dataset includes: • EmploymentDuration: Time until the participant exits their current employment (in months). Event: Indicator of whether the employment exit event has occurred (1 for event, for censored). Tasks: 1. Data Preparation: • Load the statsnew.csv dataset, including Employment Duration and Event. . Check for missing values and handle them appropriately. • Convert categorical variables into suitable numerical formats. 2. Exploratory Data Analysis: Visualize the distribution of Employment Duration using Kaplan-Meier curves stratified by key predictors (e.g., Education Level, Gender). . Estimate and plot the Kaplan-Meier survival estimates for different groups. 3. Cox Proportional Hazards Model: • • • Fit a Cox proportional hazards model with Employment Duration as the time variable and Event as the event indicator. Include predictors such as Age, Gender, EducationLevel, Income, HoursWorked, JobSatisfaction, Marital Status, etc. Assess the proportional hazards assumption using Schoenfeld residuals and graphical diagnostics. 4. Model Refinement: . • Address any violations of the proportional hazards assumption by incorporating time- dependent covariates or stratification. Perform variable selection using stepwise methods based on AIC.
Objective: Analyze the factors influencing the duration of employment (EmploymentDuration) among participants using survival analysis techniques. Assumption: Assume that the dataset includes: • EmploymentDuration: Time until the participant exits their current employment (in months). Event: Indicator of whether the employment exit event has occurred (1 for event, for censored). Tasks: 1. Data Preparation: • Load the statsnew.csv dataset, including Employment Duration and Event. . Check for missing values and handle them appropriately. • Convert categorical variables into suitable numerical formats. 2. Exploratory Data Analysis: Visualize the distribution of Employment Duration using Kaplan-Meier curves stratified by key predictors (e.g., Education Level, Gender). . Estimate and plot the Kaplan-Meier survival estimates for different groups. 3. Cox Proportional Hazards Model: • • • Fit a Cox proportional hazards model with Employment Duration as the time variable and Event as the event indicator. Include predictors such as Age, Gender, EducationLevel, Income, HoursWorked, JobSatisfaction, Marital Status, etc. Assess the proportional hazards assumption using Schoenfeld residuals and graphical diagnostics. 4. Model Refinement: . • Address any violations of the proportional hazards assumption by incorporating time- dependent covariates or stratification. Perform variable selection using stepwise methods based on AIC.
Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter4: Writing Linear Equations
Section: Chapter Questions
Problem 11CT
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Transcribed Image Text:Objective:
Analyze the factors influencing the duration of employment (EmploymentDuration) among
participants using survival analysis techniques.
Assumption:
Assume that the dataset includes:
•
EmploymentDuration: Time until the participant exits their current employment (in months).
Event: Indicator of whether the employment exit event has occurred (1 for event, for
censored).
Tasks:
1. Data Preparation:
•
Load the statsnew.csv dataset, including Employment Duration and Event.
.
Check for missing values and handle them appropriately.
•
Convert categorical variables into suitable numerical formats.
2. Exploratory Data Analysis:
Visualize the distribution of Employment Duration using Kaplan-Meier curves stratified by
key predictors (e.g., Education Level, Gender).
.
Estimate and plot the Kaplan-Meier survival estimates for different groups.
3. Cox Proportional Hazards Model:
•
•
•
Fit a Cox proportional hazards model with Employment Duration as the time variable and
Event as the event indicator.
Include predictors such as Age, Gender, EducationLevel, Income, HoursWorked,
JobSatisfaction, Marital Status, etc.
Assess the proportional hazards assumption using Schoenfeld residuals and graphical
diagnostics.
4. Model Refinement:
.
•
Address any violations of the proportional hazards assumption by incorporating time-
dependent covariates or stratification.
Perform variable selection using stepwise methods based on AIC.
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