Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 5: Difference between revisions
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first, lets make two datasets: | first, lets make two datasets: | ||
# lets work with rivers. but i'll add some random noise: new.rivers <- rivers: rnorm(n=length(rivers), mean= | # lets work with rivers. but i'll add some random noise: new.rivers <- rivers: rnorm(n=length(rivers), mean=6, sd=6) | ||
# lets also download this file: http://www.openintro.org/stat/data/nc.RData ([https://htmlpreview.github.io/?https://github.com/andrewpbray/oiLabs-base-R/blob/master/inf_for_numerical_data/inf_for_numerical_data.html documentation is here) | # lets also download this file: http://www.openintro.org/stat/data/nc.RData ([https://htmlpreview.github.io/?https://github.com/andrewpbray/oiLabs-base-R/blob/master/inf_for_numerical_data/inf_for_numerical_data.html documentation is here]) | ||
* paired t-test: | * paired t-test: | ||
Revision as of 01:03, 3 February 2017
as promised, we'll be adding much less each week.
first, lets make two datasets:
- lets work with rivers. but i'll add some random noise: new.rivers <- rivers: rnorm(n=length(rivers), mean=6, sd=6)
- lets also download this file: http://www.openintro.org/stat/data/nc.RData (documentation is here)
- paired t-test:
- i'm not going to walk through doing it by hand this week. i trust you can translate the equations in the book into R at this point
- compare our two rivers datasets using t.test()
- unpaired t-test with two vectors
- works with the rivers examples in the same way
- we can also do it with birthweight boys and girls in the nc.dataset by splitting into two vectors
- unpaired t-test with the formula notation: t.test(mpg ~ am, data=mtcars) # manual versus automatic transmission
- anova: aov(), we'll be talking about anova() later!
- returns a anova object. we can save that and then use the summary() function to give us more useful information
