Note: url corrected based on forum post at https://www.fun-mooc.fr/courses/course-v1:inria+41016+self-paced/courseware/7bf2267c336246f9b6518db624692e14/96b7ce47bd11466a9a2e63d8e8a93d99/
#+BEGIN_SRC python :session :export both :results value
#+BEGIN_SRC python :session :exports both :results value
data = pd.read_csv("https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/raw/master/data/shuttle.csv")
data
#+END_SRC
...
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@@ -181,7 +181,7 @@ data
We know from our previous experience on this data set that filtering
data is a really bad idea. We will therefore process it as such.
#+BEGIN_SRC python :session :export both :results output
#+BEGIN_SRC python :session :exports both :results output
#%matplotlib inline
pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas
import matplotlib.pyplot as plt
...
...
@@ -206,7 +206,7 @@ Let's assume O-rings independently fail with the same probability which
solely depends on temperature. A logistic regression should allow us to
estimate the influence of temperature.
#+BEGIN_SRC python :session :export both :results value
#+BEGIN_SRC python :session :exports both :results value
import statsmodels.api as sm
data["Success"]=data.Count-data.Malfunction
...
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@@ -228,7 +228,7 @@ Model Family: Binomial Df Model: 1