How can I resolve the large differences between Estimated and Actual Rows for many tables?
However, I am puzzled by why the estimated vs. actual rows are off by a roughly 10x for Table1 (Table1 in this database has about 13M rows), and way off for Table9, Table10, and Table11 as well.
I tried using DTA to find what other statistics might be needed for this statement, but it didn't find any.
That section of the query plan is on the inner side of a Nested Loops Left Semi Join.
Semi join stops looking for matches as soon as the first one is found. The optimizer modifies estimates on the inner side to match this semantic: it tries to work out how many rows it will need to process to find one row that satisfies the semi join. This adjustment is known as applying a row goal.
Queries are usually estimated on the basis that all qualifying rows will be retrieved. Using TOP, a FAST n hint, or a semi join means the row goal adjustment is applied to more closely model the behaviour.
Predicting how many rows need to be processed before the first one matches is a tricky proposition at the best of times. In this case, the inner side of the semi join is complex: the third input to the Concatenation will only be accessed if the first two do not find a match. The arrangement is complex enough that the estimate produced is unlikely to be much better than a guess, whatever statistics you provide.
Improving cardinality estimation for this query would require rewriting it to better fit the capabilities of the cardinality estimator.