# 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