I'm running logistic regressions in Python using statsmodels logit and, downstream, am calculating odds ratios for each independent variable (i.e., exp(B)).
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 safely 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? Or is a one-unit difference literally a "difference of 1.0" in the value of the predictor?
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