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I have run a GLMM model using function glmer of package lme4. But I do not know how to interpret the results. The summary of the model is:

Random effects:
 Groups Name        Variance Std.Dev.
 SITE   (Intercept) 0.7851   0.8861  
Number of obs: 126, groups:  SITE, 23

Fixed effects:
                   Estimate Std. Error z value Pr(>|z|)  
(Intercept)          0.6092     0.4725   1.289   0.1973  
ROUND2              -0.6701     0.4444  -1.508   0.1316  
ROUND3              -0.7139     0.4515  -1.581   0.1138  
ROUND4              -0.1231     0.4890  -0.252   0.8012  
ROUND5              -0.5005     0.4618  -1.084   0.2785  
ROUND6              -1.0820     0.4378  -2.471   0.0135 *
WATERSSea B          0.4442     0.6352   0.699   0.4843  
WATERSSea C          0.6276     0.7369   0.852   0.3944  
WATERSSea D         -2.9867     1.1718  -2.549   0.0108 *
ROUND2:WATERSSea B   0.6573     0.6026   1.091   0.2753  
ROUND3:WATERSSea B   0.6114     0.6075   1.006   0.3143  
ROUND4:WATERSSea B   0.3649     0.6791   0.537   0.5910  
ROUND5:WATERSSea B  -0.1535     0.6326  -0.243   0.8083  
ROUND6:WATERSSea B   0.8440     0.5926   1.424   0.1544  
ROUND2:WATERSSea C   1.4036     0.6980   2.011   0.0443 *
ROUND3:WATERSSea C   1.0290     0.6993   1.472   0.1411  
ROUND4:WATERSSea C   0.5912     0.7278   0.812   0.4167  
ROUND5:WATERSSea C   0.8681     0.7035   1.234   0.2173  
ROUND6:WATERSSea C   1.0273     0.6919   1.485   0.1376  
ROUND2:WATERSSea D   0.5698     1.1962   0.476   0.6338  
ROUND3:WATERSSea D   1.0806     1.1663   0.926   0.3542  
ROUND4:WATERSSea D  -0.1762     1.0833  -0.163   0.8708  
ROUND5:WATERSSea D   2.8976     1.4147   2.048   0.0405 *
ROUND6:WATERSSea D   2.9811     1.4150   2.107   0.0351 *

Does that mean Round 6 is significantly affecting the dependent variable? WATERSSea D in ROUND5 is significantly different from other variable? And how can I get the P value of ROUND1 as well as WATERSSea A?

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marked as duplicate by gung r May 1 at 20:14

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