Yes/no is categorical.
Categorical variables represent types of data which may be divided
into groups. Examples of categorical variables are race, sex, age
group, and educational level.
This is not a standard situation because it concerns unordered pairs. It needs a model and some analysis.
The model describes the state pairs when there is no association between states. One plausible and flexible model supposes each state $s$ is associated with a constant but unknown probability $\pi_s$. (We introduce these probabilities, and allow them ...
The fixed-effects coefficients have the interpretation of log odds ratios (and log odds for the intercept). For example, the interpretation for the coefficient of speciesTUTI is the log odds ratio for the level TUTI versus the reference level for the factor variable species.
However, note that the inclusion of the random intercepts terms complicates to a ...
There is no general rule and this is why such tables have a whole bunch of statistics associated with them:
Positive predictive value
Negative predictive value
are perhaps the most common but see Wikipedia for many more. Virtually every percentage that you can calculate (and many combinations) has some utility for some ...
There is no simple method.
Because these values supposedly have some meaning, and the correct ways of handling such variables depends a lot on what the data meansz and how you like to do this.
Assuming this is some questionnaire, it may or may not be a good idea to try to encode all variables as low/high or low/typical/high based on the value distribution, ...
The proper hypothesis and test depends on what data you have. In your question you say that all you have is "passed" and "failed". In that case, you cannot test lung capacity but only whether the same proportion passed. You could do this with a t-test of proportions.
If you can get data on their actual lung capacity (in mm or however you measure this) then ...