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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?

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