Interpreting odds ratio units I'm running logistic regressions in Python using statsmodels logit and, downstream, am calculating odds ratios for each independent variable.
I know that, conventionally, an odds ratio is interpreted per "one unit" increase in the value of the variable. I think my ultimate question is how can I determine with certainty what "one unit" really is for each variable, and if it depends upon the precision of the data for the relevant column.
One parameter, respiratory_rate, has values in integers (22, 26, 30, 12, etc.). So I think I can assume a one unit increase in respiratory_rate would be one whole number.
However, a second parameter, temperature, has values with decimal points to the tenths place (97.8, 98.4, 99.5, 100.3, etc.). Here, for the odds ratio for temperature, would the "one unit increase" be an increase in tenths, i.e., an increase of 0.1?

It's literally the derivative.
$$
odds\_ratio = \beta_0 + \beta_1x$$
$$\dfrac{\partial odds\_ratio}{\partial x} = \beta_1
$$
Then $x$ increases by one, the odds ratio increases by $\beta_1$.
Let's do a concrete example with numbers.
$$
odds\_ratio = 1 + 2x\\
\beta_0 = 1\\
\beta_1 = 2
$$
When $x=0$, $odds\_ratio = 1$.
When $x$ increases by $1$ to give $x=1$, $odds\_ratio = 3$.
When $x$ increases by $1$ again to give $x=2$, $odds\_ratio = 5$.
As an exercise, what is the odds ratio for $x = 1.5$, and how does that compare to when $x=1$ or when $x=2?$
