Course Outline

list High School / Advanced Statistics and Data Science I (ABC)

Book
  • High School / Advanced Statistics and Data Science I (ABC)
  • High School / Statistics and Data Science I (AB)
  • High School / Statistics and Data Science II (XCD)
  • High School / Algebra + Data Science (G)
  • College / Introductory Statistics with R (ABC)
  • College / Advanced Statistics with R (ABCD)
  • College / Accelerated Statistics with R (XCD)
  • CKHub: Jupyter made easy

11.14 Chapter 11 Review Questions

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require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run the following code gf_histogram(~median_income, data = collegegrads) %>% gf_facet_grid(STEM ~ .) %>% gf_labs(x = "Median Income (in thousands of dollars)")
require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run your code here
require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run your code here

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