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

sort.list()
complete.cases()

== Skipped for now == 

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

** 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]
* running quick simulations
** lets look at the relationship between mean and standard deviation on a 1 through 10 likert scale
@@ Line 1: / Line 1: @@
-* keys stuff for building confidence intervals and p-values:
+sort.list()
-** compute a sample standard error just like we did in the book, but in R
+complete.cases()
-** t.test() with one sample (build a confidence interval)
-* two things I showed in class which are super useful:
+== Skipped for now ==
-** sort.list()
-** complete.cases()
-* doing something repeatedly:
+* ordered() — really just a type of factor for ordinal data
-** 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
@@ Line 21: / Line 15: @@
 *** 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
+** lets look at the relationship between mean and standard deviation on a 1 through 10 likert scale
-** experiment by changing the size of the sample
-== Skipped for now ==
-* ordered() — really just a type of factor for ordinal data