The goal of the course is use regression analysis as tool to understand your data. To achieve this objective, the course gives the tools to understand regressions, from the simple and practical linear model, to the glm, focussing on binary outcomes (logit) and count data (poisson).
This analysis is part of the optional quiz, to understand how students understand and work with data.
A codebook for the dataset is given below:
rank: Rank by median earnings
major_code: Major code
major: Major description
major_category: Category of major
total: Total number of people with major
sample_size: Sample size of full-time, year-round individuals used for income/earnings estimates: p25th, median, p75th
p25th: 25th percentile of earnings
median: Median earnings of full-time, year-round workers
p75th: 75th percentile of earnings
perc_men: % men with major (out of total)
perc_women: % women with major (out of total)
perc_employed: % employed (out of total)
perc_employed_fulltime: % employed 35 hours or more (out of employed)
perc_employed_parttime: % employed less than 35 hours (out of employed)
perc_employed_fulltime_yearround: % employed at least 50 weeks and at least 35 hours (out of employed and full-time)
perc_unemployed: % unemployed (out of employed)
perc_college_jobs: % with job requiring a college degree (out of employed)
perc_non_college_jobs: % with job not requiring a college degree (out of employed)
perc_low_wage_jobs: % in low-wage service jobs (out of total)