# Can I conclude this from my random effects model?

I'm currently performing a random slope model to figure out whether the hierarchical factor (categorical: e.g., school A, school B ...) is biasing my results. I've used the following model/code in R:

lmer(results ~ IQ + (IQ|School), data = mydata)

Linear mixed model fit by REML ['lmerMod']
Formula: results ~ IQ  + (IQ  | School)
Data: mydata

REML criterion at convergence: -1868.1

Scaled residuals:
Min      1Q  Median      3Q     Max
-3.6625 -0.5844  0.0045  0.5824  3.1794

Random effects:
Groups     Name        Variance  Std.Dev. Corr
School     (Intercept) 0.0008229 0.02869
IQ          0.2509133 0.50091  -0.04
Residual               0.0002142 0.01464
Number of obs: 150, groups:  school, 24

Fixed effects:
Estimate Std. Error t value
(Intercept) 0.005424   0.006000   0.904
IQ          1.065126   0.115194   9.246

Correlation of Fixed Effects:
(Intr)
IQ  -0.055


Is it possible to conclude the following from this model: After controlling for the school effect, the effect/slope of IQ on test results is 1.06.

If not, how could I solve this problem statistically?