Course Outline
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segmentGetting Started (Don't Skip This Part)
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segmentStatistics and Data Science II
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segmentPART I: EXPLORING AND MODELING VARIATION
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segmentChapter 1 - Exploring Data with R
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segmentChapter 2 - From Exploring to Modeling Variation
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segmentChapter 3 - Modeling Relationships in Data
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segmentPART II: COMPARING MODELS TO MAKE INFERENCES
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segmentChapter 4 - The Logic of Inference
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segmentChapter 5 - Model Comparison with F
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5.14 Chapter 5 Review Questions
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segmentChapter 6 - Parameter Estimation and Confidence Intervals
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segmentPART III: MULTIVARIATE MODELS
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segmentChapter 7 - Introduction to Multivariate Models
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segmentChapter 8 - Multivariate Model Comparisons
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segmentChapter 9 - Models with Interactions
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segmentChapter 10 - More Models with Interactions
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segmentFinishing Up (Don't Skip This Part!)
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segmentResources
list High School / Statistics and Data Science II (XCD)
Book
5.14 Chapter 5 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