• Population: A set of people that you want to study
    • Average represented by μ (quantitative variables)
    • Standard deviation represented by σ (quantitative variables)
    • Proportion represented by p (categorical variables)
  • Sample: A group of people taken from the population from which you can make generalizations about the population
    • Average represented by x̄ (quantitative variables)
    • Standard deviation represented by s (quantitative variables)
    • Proportion represented by p̂ (categorical variables)
  • R command for standard deviation: sd(c([SET OF DATA]))
  • Discrete variables are ones that are countable, such as change, where continuous variables are ones with an infinite range of values, such as age (cannot count age because you can go infinitely small: 10 years, 2 months, 5 days, 40 seconds, 3 milliseconds, etc.)
  • Nominal variables are used to sort things into groups while ordinal variables are used to rank options
  • Histograms are used to represent data; variable on x-axis, frequency on the y-axis
    • Example: image
    • Histograms can take different shapes; symmetric, left/right skewed, peaks
  • Bar and whisker plot graph shows IQR, maximum, minimums, and outliers
    • Example: image
  • Points are considered outliers if they are less than Q1 - 1.5 * IQR or greater than Q3 + 1.5 * IQR
  • Experiments consist of two groups: treatment and control groups
    • Compare data from both groups and see if there is a statistically significant result