In this project, we gather reproduction attempts from the Challenger
study. In particular, we try to reperform some of the analysis provided
in *Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of
Failure* by *Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley*
published in *Journal of the American Statistical Association*, Vol. 84,
No. 408 (Dec., 1989), pp. 945-957 and available at
[here](https://studies2.hec.fr/jahia/webdav/site/hec/shared/sites/czellarv/acces_anonyme/OringJASA_1989.pdf)
(here is [the official JASA
webpage](http://www.jstor.org/stable/2290069)).

On the fourth page of this article, they indicate that the maximum
likelihood estimates of the logistic regression using only temperature
are: $`\hat{\alpha}=5.085`$ and $`\hat{\beta}=-0.1156`$ and their
asymptotic standard errors are $`s_{\hat{\alpha}}=3.052`$ and
$`s_{\hat{\beta}}=0.047`$. The Goodness of fit indicated for this model
was $`G^{2} = 18.086`$ with 21 degrees of freedom. Our goal is to
reproduce the computation behind these values and the Figure 4 of this
article, possibly in a nicer looking way.


[*Here is our successful replication of Dalal et al. results using
R*](challenger.pdf).

1.  Try to **replicate the computation** from Dalal et al. In case it
    helps, we provide you with twoimplementations of this case study
    but we encourage you to **reimplement them by yourself** using both
    your favourite language and an other language you do not know yet.
    -   A [Jupyter Python3 notebook](src/Python3/challenger.ipynb)
    -   An [Rmarkdown document](src/R/challenger.Rmd)
2.  Then **update the following table with your own results by
    indicating in each column:**
	-   Language: R, Python3, Julia, Perl, C...
	-   Language version:
	-   Main libraries: please indicate the versions of all the loaded
		libraries
    -   Tool: Jupyter, Rstudio, Emacs
	-   Operating System: Linux, Mac OS X, Windows, Android, ... along with
      its version
	-   $`\hat{\alpha}`$ and $`\hat{\beta}`$: Identical, Similar, Different,
Non functional (expected values are $`5.085`$ and $`-0.1156`$)
	-   $`s_{\hat{\alpha}}`$ and $`s_{\hat{\beta}}`$: Identical, Similar,
			Different, Non functional (expected values are $`3.052`$ and $`0.047`$)
	-   $`G^2`$ and degree of freedom: Identical, Similar, Different, Non
		functional (expected values are $`18.086`$ and $`21`$).
	-   Figure: Similar, Different, Non functional, Did not succeed
	-   Confidence region: Identical (to [the one obtained with R](challenger.pdf)), Similar, Quite Different, Did not succeed
 
| Language | Language version | Main libraries                                                | Tool    | Operating System               | $`\hat{\alpha}= 5.085`$ $`\hat{\beta} = -0.1156`$ | $`s_{\hat{\alpha}} = 3.052`$ $`s_{\hat{\beta}} = 0.047`$ | $`G^{2} = 18.086`$ $`dof = 21`$ | Figure    | Confidence Region | Link to the document                                                                                                                                                                                                                                                                                                         | Author      |
| -------- | ---------------- | ------------------------------------------------------------- | ------- | ----------------------------   | -------------                            | ----------                                | ----------                   | --------- | ----------   | -----------------------------------------------------------------                                                                                                                                                                                                                                                                                            | ----------- |
| R        |            3.5.1 | ggplot2 3.0.0                                                 | RStudio | Debian GNU/Linux buster/sid    | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [Rmd](src/R/challenger.Rmd), [pdf](src/R/challenger_debian_alegrand.pdf)                                                                                                                                                                                                                                                                                     | A. Legrand  |
| Python   |            3.6.4 | statsmodels 0.9.0 numpy 1.13.3 pandas 0.22.0 matplotlib 2.2.2 | Jupyter | Linux Ubuntu 4.4.0-116-generic | Identical                                | Identical                                 | Identical                    | Identical | Similar      | [ipynb](src/Python3/challenger.ipynb), [pdf](src/Python3/challenger_ubuntuMOOC_alegrand.pdf)                                                                                                                                                                                                                                                                 | A. Legrand  |
| R        |            3.5.1 | ggplot2 3.0.0                                                 | RSrudio | Windows >= 8 x64 (build 9200)  | Identical                                | Identical                                 | Identical                    | Identical | Similar      | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/8517fa92e97b3a318e653caefbfde6b5/mooc-rr/blob/master/module4/MOOC_exercice_module4.Rmd), [Pdf](https://app-learninglab.inria.fr/moocrr/gitlab/8517fa92e97b3a318e653caefbfde6b5/mooc-rr/blob/master/module4/MOOC_exercice_module4.pdf)                                                                   | M. Saubin   |
| Python   |            3.6.4 | statsmodels 0.9.0 numpy 1.15.2 pandas 0.22.0 matplotlib 2.2.3 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical                                | Identical                                 | Identical                    | Identical | Similar      | [ipynb](module4/challenger_Python_ipynb.ipynb), [pdf](module4/challenger_Python_ipynb.pdf)                                                                                                                                                                                                                                                                   |2992438755465b7fe3afd7856bde0599|
| R        |            3.4.4 | ggplot2_3.3.0                                                 | RStudio | Linux Mint 19                  | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module4/challenger_reexecuted.Rmd), [html](https://app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module4/challenger_reexecuted.html)                                                                 | b2c48a7ab4afbff5f4d26650b09eb6b4 |
| Python   |            3.6.4 | statsmodels 0.9.0 numpy 1.15.2 pandas 0.22.0 matplotlib 2.2.3 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical                                | Identical                                 | Identical                    | Identical | Similar      | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/34ea1ee296fc8711adf020d9cc2cb571/mooc-rr/blob/master/module4/challenger.ipynb), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/34ea1ee296fc8711adf020d9cc2cb571/mooc-rr/blob/master/module4/challenger.pdf)                                                                                     | 34ea1ee296fc8711adf020d9cc2cb571 |
| Matlab   | 9.6.0.1072779 (R2019a) |                                              | Matlab Live Script | Windows 10.0.18362             | Identical                                | Identical                                 | Non Functionnal              | Similar   | Did not succeed | [mlx](https://app-learninglab.inria.fr/moocrr/gitlab/34ea1ee296fc8711adf020d9cc2cb571/mooc-rr/blob/master/module4/challenger.mlx), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/34ea1ee296fc8711adf020d9cc2cb571/mooc-rr/blob/master/module4/challenger_matlab.pdf)                                                                               | 34ea1ee296fc8711adf020d9cc2cb571 |
| R        |            3.6.1 | ggplot2 3.1.1                                                 | RSrudio | Ubuntu 18.04.4 LTS             | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/a308dc99373eb1db581156a44d010769/mooc-rr/blob/master/module4/challenger_aschmide.Rmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/a308dc99373eb1db581156a44d010769/mooc-rr/blob/master/module4/challenger_aschmide.pdf)                                                                       | a308dc99373eb1db581156a44d010769 |
| R        |            3.5.3 | ggplot2_3.1.1                                                 | RStudio | Windows 10 x64 (build 18363)   | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/84848da3cce8d6b635b2e7c1749f3f0a/mooc-rr/blob/master/module4/challenger_CL.Rmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/84848da3cce8d6b635b2e7c1749f3f0a/mooc-rr/blob/master/module4/challenger_CL.pdf)                                                                                   | 84848da3cce8d6b635b2e7c1749f3f0a |
| Python   |            3.7.6 | statsmodel 0.11.1 scipy 1.4.1 pandas 1.0.3 seaborn 0.10.0     | Jupyter | Ubuntu 18.04.4 LTS             | Identical                                | Identitcal                                | Identical                    | Non functionnal | Non functionnal | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/a308dc99373eb1db581156a44d010769/mooc-rr/blob/master/module4/challenger.ipynb) [html]                                                                                                                                                                                                        | a308dc99373eb1db581156a44d010769 |
| Python   |            3.7.4 | statsmodel 0.10.1 scipy 1.3.1 pandas 0.25.1 seaborn 0.9.0     | Jupyter | Windows 10 x64                 | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/e33eb88ad13e77fcab40e23aa5b9eb7e/mooc-rr/blob/master/module4/src_Python3_challenger.ipynb))                                                                                     | e33eb88ad13e77fcab40e23aa5b9eb7e |
| Julia    |            1.4.0 | CSV v0.6.1 DataFrames v0.20.2 GLM v1.3.9                     | Weave.jl | Linux Debian 4.14.17-1 x86_64  | Identical                                | Identical                                 | Similar                      | Identical | Did not succeed | [jmd](https://app-learninglab.inria.fr/moocrr/gitlab/3e6bf7b47a05a05ae3d6af86121dcb5d/mooc-rr/blob/master/module4/challenger.jmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/3e6bf7b47a05a05ae3d6af86121dcb5d/mooc-rr/blob/master/module4/challenger.pdf), [html](https://app-learninglab.inria.fr/moocrr/gitlab/3e6bf7b47a05a05ae3d6af86121dcb5d/mooc-rr/blob/master/module4/challenger.html) | 3e6bf7b47a05a05ae3d6af86121dcb5d |
| R        |            3.6.3 | ggplot2 3.3.0                                                 | RStudio | Windows 10 x64 (build 18363)   | Similar $`\hat{\alpha}=5.08498`$ $`\hat{\beta}=-0.11560`$ | Similar $`s_{\hat{\alpha}} = 3.05247`$ $`s_{\hat{\beta}} = 0.04702`$ | Identical | Identical | Identical | [Rmd] (https://app-learninglab.inria.fr/moocrr/gitlab/d581efc242e1e06dfa8d0dea8ee470e0/mooc-rr/blob/master/module4/Exo1_Module4.Rmd), [pdf] (https://app-learninglab.inria.fr/moocrr/gitlab/d581efc242e1e06dfa8d0dea8ee470e0/mooc-rr/blob/master/module4/Exo1_Module4.pdf) | d581efc242e1e06dfa8d0dea8ee470e0 |
| Python   |            3.7.7 | statsmodel 0.11.1 scipy 1.4.1 pandas 1.0.3 seaborn 0.10.0     | Jupyter | Linux Mint 19.1 Cinnamon       | Identical                                | Identitcal                                | Identical                    | Similar   | Similar       | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/1affb6a270b94ae1aa2914210661b070/mooc-rr/blob/master/module4/exo/challenger.ipynb) [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/1affb6a270b94ae1aa2914210661b070/mooc-rr/blob/master/module4/exo/challenger.pdf)                                                                             | 1affb6a270b94ae1aa2914210661b070 |
| Python   |            3.6.4 | statsmodels 0.9.0 numpy 1.15.2 pandas 0.22.0 matplotlib 2.2.3 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical                                | Identical                                 | Identical                    | Identical | Identical    | [ipynb] (https://app-learninglab.inria.fr/moocrr/gitlab/d3ffe1dda057aedb6d37daa14d4dce86/mooc-rr/blob/master/module4/src_Python3_challenger.ipynb), [pdf] (https://app-learninglab.inria.fr/moocrr/gitlab/d3ffe1dda057aedb6d37daa14d4dce86/mooc-rr/blob/master/module4/src_Python3_challenger.pdf) |  d3ffe1dda057aedb6d37daa14d4dce86 |  