Merge pull request #6626 from EnterpriseDB/docs/edits_to_query_advisor_pr6540

Edits to Query Advisor PR6540

Author: ebgitelman

advocacy_docs/pg_extensions/query_advisor/using.mdx

@@ -13,7 +13,7 @@ Performs a global index suggestion.
 
 By default, only predicates filtering at least 1000 rows and 30% of the rows in average are considered. You can use the `min_filter` and `min_selectivity` parameters to override the default. 
 
-The function generates the one- and two-column index candidates based on the predicates it collects. It replans all related workload queries in the presence of the hypothetical index with respect to each candidate. It recommends the list of indexes that bring the most value to the workload. It also shows the estimated index size, estimated percentage cost reduction, total cost, absolute benefit and query ID's of the benefited queries in the workload. You can decide, based on the size and benefit ratio, which indexes are the most useful for you.
+The function generates the one- and two-column index candidates based on the predicates it collects. It replans all related workload queries in the presence of the hypothetical index with respect to each candidate. It recommends the list of indexes that bring the most value to the workload. It also shows the estimated index size, estimated percentage cost reduction, total cost, absolute benefit, and query IDs of the benefited queries in the workload. Based on the size and benefit ratio, you can determine the indexes that are the most useful for you.
 
 For example:
 
@@ -35,7 +35,7 @@ By default, `min_err_estimate_num` and `min_err_estimate_ratio` are set to `0`.
 
 The function generates potential candidates from the multi-column filters of your queries. Then, these candidates are processed by exploring different possible combinations. Currently the focus is on statistics for two columns at a time. 
 
-It also shows the weights to each candidate. Weights are based on how many queries would benefit from those extended statistics and what the execution cost of the queries would be. It shows query ID's of the benefited queries for which the recommendations are beneficial.
+It also shows the weights to each candidate. Weights are based on how many queries would benefit from those extended statistics and what the execution cost of the queries would be. It shows query IDs of the benefited queries for which the recommendations are beneficial.
 
 For example: