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 | | R | 3.6.3 | ggplot2 3.3.0 | RStudio | Windows 10 x64 (build 18363) | Identical | Identical | Identical | Identical | Identical | [Rmd] (https://app-learninglab.inria.fr/moocrr/gitlab/3e3d09557efde0d973c3f246d977cc35/mooc-rr/blob/master/module4/Gullstrand_src_R_challenger.Rmd)| https:// 3e3d09557efde0d973c3f246d977cc35 | | R | 3.5.1 | ggplot2 3.3.0 | RStudio | Windows 10 x64 (build 18363) | Identical | Identical | Identical | Identical | Identical | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/525f77b1f86b1bcc7fc77168921eb737/mooc-rr/blob/master/challenger.Rmd) [html](https://app-learninglab.inria.fr/moocrr/gitlab/525f77b1f86b1bcc7fc77168921eb737/mooc-rr/blob/master/challenger.html) | | Python | 3.7.1 | statsmodels 0.10.1 scipy 1.1.0 pandas 0.25.3 seaborn 0.9.0 | Jupyter | Windows 10 x64 (build 17134) | Similar $`\hat{\alpha}=5.0850'$ | Identical | Identical | Identical after code change | Identical after code change | [ipynb] (https://app-learninglab.inria.fr/moocrr/gitlab/65f0b71b8c107dc7c99fe00b869170e1/mooc-rr/blob/master/module4/src_Python3_challenger.ipynb), | 65f0b71b8c107dc7c99fe00b869170e1 | | Python | 3.6.4 | statsmodels 0.9.0 scipy 1.1.0 pandas 0.22.0 seaborn 0.8.1 | Jupyter | Windows 10 x64 (build 18363) | Identical | R | 3.5.1 | ggplot2_3.1.0 | RStudio | Windows 10 Pro 1909 - 64 bits | Similar (5.08498 , -0.1156) | Similar ( ,3.052 ,0.04702 ) | Identical |Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/de98852947736bd9e2f70971a6ed56d0/mooc-rr/blob/master/module4/ex4.ipynb) | de98852947736bd9e2f70971a6ed56d0 | | R | 4.0.0 | ggplot2 3.3.0 | RStudio | Manjaro Linux | Identical | Identical | Identical | Identical | Identical | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/55066b9debf86be253468bbb91c0a965/mooc-rr/blob/master/module4/challenger.Rmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/55066b9debf86be253468bbb91c0a965/mooc-rr/blob/master/module4/challenger.pdf) | 55066b9debf86be253468bbb91c0a965 | | R | 4.0.0 | ggplot2_3.3.1 | RStudio | Windows 10 x64 (build 18363) |Identical | Identical | Identical | Identical | Identical | 88d1c655b790d6dc02a9352a7e3c81c45720f715 | Tegegne (Note I edited the data then it works) | Python | 3.6.4 | statsmodels 0.9.0 numpy 1.13.3 pandas 0.22.0 matplotlib 2.2.2 seaborn 0.8.1 | Jupyter | Windows 10 Pro 1809 - 64 bits | Identical | Identical | Identical | Identical | Similar | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/724aaab995271739bac044894b5182b9/mooc-rr/blob/master/module4/src_Python3_challenger.ipynb) | 724aaab995271739bac044894b5182b9 | | Python | 3.7.4 | numpy 1.19.0 scipy 1.5.0 statsmodels 0.10.1 | Jupyter | Windows 10 x64 18362.900 | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/2c1cfe3b7099fc1c57ee95d52ee21f93/mooc-rr/blob/master/module4/src_Python3_challenger.ipynb) | 2c1cfe3b7099fc1c57ee95d52ee21f93 | | Python | 3.6.4 | numpy 1.15.0 pandas 0.22.0 seaborn 0.8.1 matplotlib 2.2.3 statsmodels 0.9.0 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical | Identical | Identical | Identical | Similar | [ipynb](https://app-learninglab.inria.fr/moocrr/jupyter/user/b4d31d1037af989f9880e6ac666123c2/notebooks/work/module4/exercice_module5.ipynb), [pdf](https://app-learninglab.inria.fr/moocrr/jupyter/user/b4d31d1037af989f9880e6ac666123c2/notebooks/work/module4/exercice_module5.pdf) | b4d31d1037af989f9880e6ac666123c2 | | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 seaborn 0.8.1 statsmodels 0.9.0 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/26ab366f4d50047285d9a557b01018bd/mooc-rr/blob/master/module4/Analyse%20Challenger.ipynb) | 26ab366f4d50047285d9a557b01018bd | | Python | 3.7.3 | statsmodels 0.12.0 numpy 1.16.4 pandas 0.25.0 matplotlib 3.1.1 seaborn 0.9.0 | Jupyter | Windows 7 6.1.7601 - 64 bits | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/a6a8e0f601822270ca754f29f77749b6/mooc-rr/blob/master/module4/challenger.ipynb) | a6a8e0f601822270ca754f29f77749b6 | | Python | 3.7.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 seaborn 0.8.1 statsmodels 0.9.0 | Jupyter | Windows 10 x64 (build 17134) | Identical | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 scipy 1.1.0 seaborn 0.8.1 statsmodels 0.9.0 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/82e5a278bec86aaf9c3baaadd03ac681/mooc-rr/blob/master/module4/challenger_Windows_TMalou.ipynb) | a6a8e0f601822270ca754f29f77749b6 | | R | 4.0.3 | ggplot2_3.3.3 | RStudio | Windows 10 x64 (build 19041) | Identical | Identical | Identical | Identical | Identical | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/813ebc94a0e1e9bad5e0398b9c5c31c1/mooc-rr/blob/master/module4/Replication__regression_haas.Rmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/813ebc94a0e1e9bad5e0398b9c5c31c1/mooc-rr/blob/master/module4/Replication__regression_haas.pdf) | 813ebc94a0e1e9bad5e0398b9c5c31c1 | | R | 4.0.3 | ggplot2_3.3.2 | RStudio | Windows 7 x64 (build 7601) | Identical |Identical | Identical | Identical | Identical |[Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/42229b8d9233ebe04805dfd660381627/mooc-rr/blob/master/module4/ChallengerRepro.Rmd), [pdf](https://app-learninglab.inria.fr/moocrr/gitlab/42229b8d9233ebe04805dfd660381627/mooc-rr/tree/master/module4/ChallengerRepro.pdf)|42229b8d9233ebe04805dfd660381627| | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 scipy 1.1.0 seaborn 0.8.1 statsmodels 0.9.0 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/720d0853c18a70320e63c05851c89be9/mooc-rr/blob/c3bff91e8a51f8eb1706123aad9c1e0d0806ad8f/module4/challenger.ipynb) | 720d0853c18a70320e63c05851c89be9 | | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 statsmodel 0.9.0 | Jupyter | Linux Ubuntu 4.4.0-164-generic | Identical | Identical | Identical | Identical | Identical | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/c0effb0c6dcf58f313629e385bf91920/mooc-rr/blob/master/module4/challenger.ipynb) | c0effb0c6dcf58f313629e385bf91920 | | Python | 3.8.8 | matplotlib 3.3.4 numpy 1.19.1 pandas 1.2.3 statsmodel 0.12.2 seaborn 0.11.1 | Orgmode | MacOS BigSur 11.2.1 | Identical | Identical | Identical | Identical | Identical | [org](https://app-learninglab.inria.fr/moocrr/gitlab/c4aa83a2eb0510a399feb4e7c1611960/mooc-rr/blob/master/module4/exo1_python_fr.org) | c4aa83a2eb0510a399feb4e7c1611960 | | Python | 3.9.0 | matplotlib 3.3.4 numpy 1.20.1 pandas 1.2.3 statsmodels 0.12.2 seaborn 0.11.1 | Jupyter | Linux Ubuntu 5.8.0-44-generic | $\hat{\alpha}=5.0850$ $\hat{\beta}=-0.1156$ | Identical | Identical | Identical | problems with the plots | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/678362a9c2b5d46a6a161a762a6e18e0/mooc-rr/blob/master/module4/repro.ipynb) | 678362a9c2b5d46a6a161a762a6e18e0 | |R | 4.0.3 | ggplot2_3.3.3 |RStudio |Windows 10 x64 (build 18362) |Similar |Similar | Identical |Identical |Identical |[PDF](https://app-learninglab.inria.fr/moocrr/gitlab/0e57e1b8e1a23d7aaeee34c5821213f6/mooc-rr/blob/master/module4/src_R_challenger_VFC.pdf)|1174e0f05f745ff7cf7cd4efeeedce0369a1d57c| | R | 4.0.4 | ggplot2_3.3.3 | RStudio | Windows 10 x64 (build 19042.867) | | | | | | | 2003cd7fa4edd13b96010441bce122a | | R | 4.1.0 | ggplot2_3.3.3 | Rstudio |Windows 10 x64 (build 19041) | Identical | Identical | Identical |Identical | Identical | [Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/ea4f5ba1b97cd5a14653d4641234245a/mooc-rr/blob/master/module4/exo5_fr_Replication.Rmd), [PDF](https://app-learninglab.inria.fr/moocrr/gitlab/ea4f5ba1b97cd5a14653d4641234245a/mooc-rr/blob/master/module4/exo5_fr_Replication.pdf) | ea4f5ba1b97cd5a14653d4641234245a | | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 statsmodel 0.9.0 seaborn 0.8.1 | Jupyter | Windows 10 x64 (build 19042) | Identical | Identical | Identical | Very slightly changed | Slightly shifted upwards at low temperature | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/7eba932125d7468e05c00632ef18215f/mooc-rr/blob/master/module4/src_Python3_challenger__1_-Copy1.ipynb) | 7eba932125d7468e05c00632ef18215f | | R | 3.6.1 | ggplot2_3.1.1 | Rstudio |Windows >= 8 x64 (build 9200) | Identical | Identical | Identical | Identical | Identical |[Rmd](https://app-learninglab.inria.fr/moocrr/gitlab/36cf6267d1f8856c85d87f0f287ea039/mooc-rr/blob/master/module4/EXO1.Rmd) [Pdf](https://app-learninglab.inria.fr/moocrr/gitlab/36cf6267d1f8856c85d87f0f287ea039/mooc-rr/blob/master/module4/test.pdf) | 36cf6267d1f8856c85d87f0f287ea039 | | Python | 3.6.4 | matplotlib 2.2.3 numpy 1.15.2 pandas 0.22.0 statsmodel 0.9.0 seaborn 0.8.1 | Jupyter | Linux Ubuntu SMP Fri Sep 13 12:02:50 UTC 2019 | Identical | Identical | Identical | Similar | Similar | [ipynb](https://app-learninglab.inria.fr/moocrr/gitlab/2207ee6c4b0763c29ab8f98156e3343c/mooc-rr/blob/master/module4/Module4-exercice1.ipynb) | @2207ee6c4b0763c29ab8f98156e3343c | Python | 3.6.5 | matplotlib 3.1.1 numpy 1.19.1 pandas 1.0.3 statsmodel 0.12.2 seaborn 0.9.0 | Jupyter | MacOS Catalina 10.15.7 | Identical | Identical | Identical | Similar | Similar | [pdf](module4/Module4-exercice1.pdf) | @2207ee6c4b0763c29ab8f98156e3343c