Interpretation of mixed model coefficients

I ran a mixed model regression and obtained the following output. Because I log-transformed the concentration variable, I interpreted the coefficient as follows: the mean concentration is reduced $1.5$ percent each year. The years run from 1993-2010.

Can say also state that the mean concentration is reduced by $26\%$ ($0.01509\cdot 17$) over the $17$ year period? Why or why not?

Why do most people generally state $1$ unit increase in $X$ corresponds to a certain number change in $Y$ rather than infer change over the entire period?

baggerTrend <-  lme(Log.Qconc   ~   Yearf,  random=(~Yearf|MineID), Bagger)

Random  effects:
Formula:    ~Yearf  |   MineID
Structure:  General positive-definite,  Log-Cholesky    parametrization
StdDev    Corr
(Intercept) 0.209172851 (Intr)
Yearf       0.000374953 -0.478
Residual    0.785367538

Fixed effects:  Log.Qconc ~ Year
Value   Std. Error    DF      t-value       pvalue
Intercept   33.71122    6.3901     2762      5.275538       0
Year        -0.01509    0.003193   2762     -4.724678       0

• If your dependent variable in the analysis is log-transformed concentration, then the coefficient for year is telling you that log-transformed concentration decreases by 0.015 every year, the coefficient is on the same scale as your DV, which is not percentages in this case. Jul 12, 2012 at 1:38
• Also, if you did not mean-center Year, and used values like 1993, 1994, etc., then the coefficients may be nonsensical, because the model is fitted for year==0. Jul 12, 2012 at 1:40
• @Marius: I interpreted the percent change/decrease based on information from this website: ats.ucla.edu/stat/sas/faq/sas_interpret_log.htm. I am 90% sure but I can be wrong. Jul 12, 2012 at 4:06
• Just had a quick look, and found this: sportsci.org/resource/stats/logtrans.html. Log-transformed coefficients do closely approximate percentages, but only when they are small. Jul 12, 2012 at 4:44
• @Amateur so your question is about the interpretation of a regression line and has nothing to do with the mixed model ? Jul 12, 2012 at 5:03

I think that would be enough to consider exp coefficient and interpret it this way: the unit change of the regressor x1, creates an increase of exp (b1) of the dependent variable

• Can you expand on this as it does not seem to add very much to the comments? Nov 16, 2016 at 15:53
• {\displaystyle \log(Y)=a+bX} \log(Y)=a+bX {\displaystyle Y=e^{a}e^{bX}} Y=e^{a}e^{{bX}}) Nov 16, 2016 at 17:27