Editing Logistic regression (section)

===Model evaluation===
The logistic regression analysis gives the following output.

{| class="wikitable"
|-
!   !! Coefficient!! Std. Error !! ''z''-value  !! ''p''-value (Wald)
|- style="text-align:right;"
! Intercept (''β''<sub>0</sub>)
| −4.1 || 1.8  || −2.3   || 0.021
|- style="text-align:right;"
! Hours (''β''<sub>1</sub>)
| 1.5     || 0.9
   || 1.7   || 0.017
|}

By the [[Wald test]], the output indicates that hours studying is significantly associated with the probability of passing the exam (<math>p = 0.017</math>). Rather than the Wald method, the recommended method<ref name="NeymanPearson1933">{{citation | last1 = Neyman | first1 = J. | author-link1 = Jerzy Neyman| last2 = Pearson | first2 = E. S. | author-link2 = Egon Pearson| doi = 10.1098/rsta.1933.0009 | title = On the problem of the most efficient tests of statistical hypotheses | journal = [[Philosophical Transactions of the Royal Society of London A]] | volume = 231 | issue = 694–706 | pages = 289–337 | year = 1933 | jstor = 91247 |bibcode = 1933RSPTA.231..289N | url = http://www.stats.org.uk/statistical-inference/NeymanPearson1933.pdf | doi-access = free }}</ref> to calculate the ''p''-value for logistic regression is the [[likelihood-ratio test]] (LRT), which for these data give <math>p \approx 0.00064</math> (see {{slink||Deviance and likelihood ratio tests}} below).