The Likelihood Ratio Tests report is useful when you want to decide if a term in the model is significant. The Parameter Estimates report is useful when you want to know if an estimate is significant.

Categorical predictors / factors appear in the linear model in a way such that the estimates across all the levels must sum to zero ("effect parameterization"). Therefore, the estimate of the parameter for the last level is equal to the negative of the sum of the other estimates. You can click the red triangle at the top and select Estimates > Expanded Estimates. (I think)

Logworth is the negative log (base 10) of the p-value. (It is the p-function of the p-value, like pH is the p-function of the Hydronium activity.) Because p-values range between zero and one, sometimes they differ by many orders of magnitude. Some users find logworth easier to gauge the significance of estimates in such cases. A change of 1 logworth is a change of one order of magnitude. Note that such users often consider logworth = 2 (p-value = 0.01) a reasonable reference for comparison but you should always determine the level of significance that is meaningful to you.

Learn it once, use it forever!