# Interpreting multivariate regression parameters

Your Bayesian model explored the dependence of typical trail volume on weekday status $$X$$i and temperature $$Z$$i: $$m$$i $$= a + b X$$i $$+ c Z$$i. A summary() of your RJAGS model simulation provides posterior mean estimates of parameters $$a$$, $$b$$, and $$c$$:

> summary(rail_sim_2)
Mean      SD Naive SE Time-series SE
a     36.592 60.6238 0.606238        4.19442
b[1]   0.000  0.0000 0.000000        0.00000
b[2] -49.610 23.4930 0.234930        0.55520
c      5.417  0.8029 0.008029        0.05849
s    103.434  7.9418 0.079418        0.11032


For example, the posterior mean of $$c$$ indicates that for both weekends and weekdays, typical rail volume increases by ~5.4 users for every 1 degree increase in temperature. Which of the following interpretations of $$b$$ (represented here by b[2]) is the most accurate?