Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 4

• keys stuff for building confidence intervals and p-values:
  • compute a sample standard error just like we did in the book, but in R
  • t.test() with one sample (build a confidence interval)

• two things I showed in class which are super useful:
  • sort.list()
  • complete.cases()

• doing something repeatedly:
  • just define a function and then apply it to a list of things
  • if you to output something in the middle: you use the print() function

• briefly covered:
  • distribution functions: lets focus on *unif(): the key is on page 222 of Verzani
    • The “d” functions return the p.d.f. of the distribution
      • dunif(x=1, min=0, max=3) # 1/3 of the area is the to the left 1
    • The “p” functions return the c.d.f. of the distribution.
      • dunif(q=2, min=0, max=3) #1/(b-a) is 2/3
    • The “q” functions return the quantiles.
      • qunif(p=0.5, min=0, max=3) # half way between 0 and 3
    • The “r” functions return random samples from a distribution.
      • runif(n=1, min=0, max=3) # a random value in [0,3]

• doing simple simulations with random data
  • runif()
  • rnorm()

• running quick simulations
  • lets look at the relationship between mean and standard deviation on a 1 through 10 likert scale

Skipped for now

• ordered() — really just a type of factor for ordinal data