(here is [http://www.jstor.org/stable/2290069](the official JASA
webpage)).
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 [[https://studies2.hec.fr/jahia/webdav/site/hec/shared/sites/czellarv/acces_anonyme/OringJASA_1989.pdf][here]] (here
is [[http://www.jstor.org/stable/2290069][the official JASA webpage]])
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
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**](file:challenger.pdf).
*[[file:challenger.pdf][Here is our successful replication of Dalal et al. results using R]]*.
In case it helps, we provide you with two implementations of this case
study but we encourage you to **reimplement them by yourself** using both
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](file:src/Python3/challenger.ipynb)
- An [Rmarkdown document](file:src/R/challenger.Rmd)
- A [[file:src/Python3/challenger.ipynb][Jupyter Python3 notebook]]
- An [[file:src/R/challenger.Rmd][Rmarkdown document]]
Then **update the [meta-study result table available
here](file:results.org) with your own results**.
Then *update the [[file:results.org][meta-study result table available here]] with your own
