How to know if results of linear fit are significantly different? I have collected some device measurements that require a linear fit to obtain physical constants that characterize the device under test.
I have slope, intercept, and their uncertainties, calculated according to the code from scipy below:

I measured the device in few different conditions. The conditions change the slope and intercept, but the uncertainties overlap quite a lot.
How can I know if the condition actually influences the slope and intercept? Note that I have small background in statistics, so I really appreciate beginner-friendly answers.

Let's say you want to compare the slope and intercept with and without condition A.  Create an aggregate data set with and without condition A, and create a new variable cond.A = 0 if not in condition A and 1 if in condition A.  (I will assume y is the response variable and x is the explanatory variable here.)
Now, instead of running a bivariate model (with just slope for x and intercept), run a model with x, cond.A, and x * cond.A.  (This may be referred to as a dichotomous moderation model.)
Now, if the coefficients (partial slopes) for cond.A and x * cond.A are non significant, then you may assume that the slopes are practically equivalent, and thus using one slope and one intercept (from the model from the aggregate data) will suffice.  If cond.A is significant, then this means the intercepts in the conditions are different, and if x*cond.A is significant, then this means the slopes are different.
Hope this helps.
