Your outcome of interest is the success probability of success. Therefore, you want some kind of probability model that estimates the probability of a success for a given perch height.
Linear regression in this case would be a linear probability model. While people do use this, there are issues, among them being that illegal probabilities exceeding $1$ or below $0$ can be predicted.
More reasonable might be a logistic regression, which applies a clever transformation to your predictions to squeeze them into legal probability values.
Any decent statistical software will have a logistic regression option that outputs coefficient estimates along with confidence intervals and p-values. You interpret the coefficients similar to how you would interpret them in a linear regression, except that they explicitly describe changes in log-odds. Simplified, this means that a positive coefficient corresponds to an increase in probability while a negative coefficient corresponds to a decrease in probability.
You do have to watch out for how you code your binary variable. Typical would be to take failures as $0$ and successes as $1$. If you do not do this coding explicitly and rely on your software to interpret a string input, you will want to know how that conversion occurs.