For today’s lab, we’ll take a closer look at logistic regression. There’s a huge range of backgrounds in this class, so some of you are probably pretty familiar with logistic regression, and it may be totally new to others! So today, you have two options. The first is to work through another OpenIntro tutorial, and the second is to do a case study on your own. You only need to do one for the lab to be considered complete today.
In this tutorial option, you’ll explore:
If you choose this option, you should include screenshots of the two graphs that best display the results of the model. This is a subjective decision!
If you feel pretty confident with logistic regression (or are just sick of the tutorials) you can choose to extend the demo from class on your own! You should include at least two variables in your model.
Included here is the rough outline of steps you should follow. In your write-up, you should include (1) an EDA graph, (2) A Model Fit Summary Graph, and (3) a short (2-3 sentence) conclusion.
# remotes::install_github("OpenIntroStat/openintro")
library(openintro)
email## # A tibble: 3,921 x 21
## spam to_multiple from cc sent_email time image attach
## <fct> <fct> <fct> <int> <fct> <dttm> <dbl> <dbl>
## 1 0 0 1 0 0 2012-01-01 01:16:41 0 0
## 2 0 0 1 0 0 2012-01-01 02:03:59 0 0
## 3 0 0 1 0 0 2012-01-01 11:00:32 0 0
## 4 0 0 1 0 0 2012-01-01 04:09:49 0 0
## 5 0 0 1 0 0 2012-01-01 05:00:01 0 0
## 6 0 0 1 0 0 2012-01-01 05:04:46 0 0
## 7 0 1 1 0 1 2012-01-01 12:55:06 0 0
## 8 0 1 1 1 1 2012-01-01 13:45:21 1 1
## 9 0 0 1 0 0 2012-01-01 16:08:59 0 0
## 10 0 0 1 0 0 2012-01-01 13:12:00 0 0
## # … with 3,911 more rows, and 13 more variables: dollar <dbl>, winner <fct>,
## # inherit <dbl>, viagra <dbl>, password <dbl>, num_char <dbl>,
## # line_breaks <int>, format <fct>, re_subj <fct>, exclaim_subj <dbl>,
## # urgent_subj <fct>, exclaim_mess <dbl>, number <fct>