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Well, it's actually pretty much the same as for the random intercepts model. So for the fixed part, our estimate of βk is significant at the 5% level if the modulus of βk divided by its standard error is >1.96, so that's also just the same as the single level regression model. For the random part, again we need to use the likelihood ratio test, so this time we're comparing the model with u1x1 to the model without u1x1. So we're basically just comparing the random slope model, that's the model with u1x1, with the random intercept model, that's the model without u1x1. So those two models are exactly the same in terms of explanatory variables, the only difference is that the random slopes model has this u1x1in it. So again the test statistic is 2 times the log likelihood of the first model minus the log likelihood of the second model and this time we have 2 degrees of freedom because we've got 2 extra parameters in the first model compared to the second model: we've got σ2u1 this time and σu01. So we compare the test statistic against the 2(2) distribution with 2 degrees of freedom, and again we can divide the p-value by 2 if we want. So the null hypothesis is that σ2u1 and σu01 are both 0 and if that's the case than a random intercept model would be more appropriate than a random slope model. 2b1af7f3a8