A reproducibly simulated cross-sectional wellbeing survey for descriptive,
correlation, regression and missing-data examples. life_satisfaction
responds meaningfully to its predictors: higher stress lowers it, more sleep
and exercise raise it, log-income has a real positive effect (about +0.4 per
natural-log unit), and age has a mild inverted-U (peaking in mid-life).
education is an ordered factor. Income is missing more often at higher
stress (missing at random); because income genuinely predicts the
outcome, this missingness is informative for the missing-data examples.
Format
A data frame with 300 rows and 9 variables:
- id
Respondent identifier.
- region
Region of residence:
North,South,EastorWest.- age
Age in years.
- education
Highest education level (ordered factor:
secondary<undergraduate<postgraduate).- income
Annual income; contains missing values (more at high stress).
- stress
Self-reported stress (1-7).
- sleep_hours
Typical hours of sleep per night; contains missing values.
- exercise_days
Days of exercise per week (0-7); contains missing values.
- life_satisfaction
Life satisfaction (1-7); contains missing values.