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

From CommunityData

- 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]

- The “d” functions return the p.d.f. of the distribution

- distribution functions: lets focus on *unif(): the key is on page 222 of Verzani

- 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[edit]

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