Data-Centric Statistical Inference Using R and Tidyverse/

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EDA for a Numerical Explanatory Variable

EDA for a Numerical Explanatory Variable

Learn about analyzing numerical data to make observations that will help in regression.

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Typing out all these summary statistic functions in summarize() would be long and tedious. Instead, let’s use the convenient skim() function from the skimr package. This function takes in a data frame, skims it, and returns the commonly used summary statistics. Let’s take our evals_ch5 data frame, select() only the outcome and explanatory variables teaching score and bty_avg, and pipe them into the skim() function:

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evals_ch5 %>% 
    select(score, bty_avg) %>% skim()

For the numerical variables teaching score and bty_avg, it returns:

  • n_missing: This is the number of missing values.

  • complete_rate: This is the number of non-missing or complete values.

  • mean: This is the average.

  • sd: This is the standard deviation.

  • p0: The 0th percentile is the value at which 0% of the observations are smaller than it ...