X-Git-Url: https://code.communitydata.science/stats_class_2019.git/blobdiff_plain/6cb9e84a24158618e4751bd67f49c74ce915dc88..6daad5c6d32c7e66729a5444709e1dd4de82b1e0:/problem_sets/week_06/ps6-worked-solution.html?ds=sidebyside diff --git a/problem_sets/week_06/ps6-worked-solution.html b/problem_sets/week_06/ps6-worked-solution.html index a0c9e48..d182029 100644 --- a/problem_sets/week_06/ps6-worked-solution.html +++ b/problem_sets/week_06/ps6-worked-solution.html @@ -324,11 +324,11 @@ library(ggridges) 
ridge_plot <- ggplot(data=df, aes(x=weeks_alive, y = dose)) + geom_density_ridges(jittered_points = T, fill = 'orange')
 ridge_plot
## Picking joint bandwidth of 10.5
-

+

# add a fancy minimalist theme to make it prettier:
 ridge_plot + theme_minimal()
## Picking joint bandwidth of 10.5
-

+

A two sample t-test assumes independence and normality. An ANOVA assumes independence, normality, and equal variance. It’s a bit tough to tell, but the overall assumption of equal variance seems reasonable. Normality is a bit of a hard sell within groups or overall. Nevertheless, most analysts would march ahead with the analysis despite these violations of assumptions. We can discuss how you might think and talk about this in class.

The global mean is

mean(df$weeks_alive)