Editing 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
** write a function to repeatedly take the minimum from a sample
** experiment by changing the size of the sample

== Skipped for now == 

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