Editing Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 5
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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=100, sd=100) | ||
# 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: | ||
** 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 | ** 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 | ** 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 | * 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! | * anova: aov(), we'll be talking about anova() later! | ||
** returns | ** returns a anova object. we can save that and then use the summary() function to give us more useful information | ||
** we can see that the results are very similar with the two group example! | ** we can see that the results are very similar with the two group example! | ||