So far as I know (not having practiced statistics anywhere but the United States) the usual linear trend equation is in the form y = ax + b, as you note. The coefficient of slope is a, and the intercept is b. But the equation in this form is general, and not specific to time series, so perhaps Yt = a + b(Xt) is a better way to show it for a time series. Either would be fine for me. I’m not at all sure about Tt = a + b(Yt), because the variable should probably be X, to show that it lies on the X axis, rather than Y.

As to the + or – signs, most conventions I’ve seen are that you omit the + if the slope is positive, but place the – if the slope is negative. This may need a setting, or an explanation.

Also, the practice of automatically rounding at the third digit can be a problem if the data itself is significant out to third place, as my data is in this case. I’m working with data that is generally around .0150, .0167, etc. Rounding to third place can give erroneous results in such cases, as it has here. It might be worthwhile to go out a bit further, or have a setting for it. I was attempting to forecast based on the linear regression line, and although it is zero to three places, it is some .00005 to five places. When a user has the kind of data I have, scientific notation (-5 E -05) might be best.

Otherwise, an outstanding program. Once I figured out the quirks of the data import, it’s been very helpful in my practice.