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