You are SQL Tutor that fixes the student query. Given a database schema, a question, and SQL query generated by student, its response in database and the feedback on the correctness of the query. Based on the Feedback, generate a fixed sql that correctly aligns with the intent of question. Examples: database schema : table movies , columns = [ movies.director_name ( text | values : Esteban Sapir , Oskar Roehler ) , movies.movie_popularity ( integer | values : 105 , 23 ) , movies.movie_id ( integer | primary key | values : 1 , 2 ) , movies.movie_title ( text | values : La Antena , Elementary Particles ) , movies.director_id ( text | values : 131 , 73 ) , movies.movie_release_year ( integer | values : 2007 , 2006 ) , movies.movie_url ( text ) , movies.movie_title_language ( text | values : en ) , movies.director_url ( text ) , movies.movie_image_url ( text ) ] table ratings , columns = [ ratings.movie_id ( integer | values : 1066 , 1067 ) , ratings.rating_score ( integer | values : 3 , 2 ) , ratings.rating_id ( integer | values : 15610495 , 10704606 ) , ratings.user_id ( integer | values : 41579158 , 85981819 ) , ratings.critic ( text ) , ratings.rating_url ( text ) , ratings.rating_timestamp_utc ( text | values : 2017-06-10 12:38:33 , 2014-08-15 23:42:31 ) , ratings.critic_likes ( integer | values : 0 , 1 ) , ratings.critic_comments ( integer | values : 0 , 2 ) , ratings.user_trialist ( integer | values : 0 , 1 ) ] table lists , columns = [ lists.list_title ( text | values : Headscratchers ) , lists.list_movie_number ( integer | values : 5 , 3 ) , lists.list_description ( text ) , lists.list_id ( integer | primary key | values : 1 , 2 ) , lists.user_id ( integer | values : 88260493 , 45204418 ) , lists.list_comments ( integer | values : 3 , 2 ) , lists.list_url ( text ) , lists.list_followers ( integer | values : 5 , 1 ) , lists.list_third_image_url ( text ) , lists.list_cover_image_url ( text ) ] table ratings_users , columns = [ ratings_users.user_id ( integer | values : 41579158 , 68654088 ) , ratings_users.user_subscriber ( integer | values : 0 , 1 ) , ratings_users.user_trialist ( integer | values : 0 , 1 ) , ratings_users.user_has_payment_method ( integer | values : 0 , 1 ) , ratings_users.user_eligible_for_trial ( integer | values : 1 , 0 ) , ratings_users.rating_date_utc ( text | values : 2017-06-10 , 2012-10-02 ) , ratings_users.user_cover_image_url ( text ) , ratings_users.user_avatar_image_url ( text ) ] table lists_users , columns = [ lists_users.list_id ( integer | primary key | values : 192287 , 192313 ) , lists_users.user_id ( integer | primary key | values : 2385 , 15264 ) , lists_users.user_trialist ( integer | values : 1 , 0 ) , lists_users.user_has_payment_method ( text | values : 1 , 0 ) , lists_users.user_subscriber ( integer | values : 1 , 0 ) , lists_users.user_eligible_for_trial ( text | values : 0 , 1 ) , lists_users.user_avatar_image_url ( text ) , lists_users.user_cover_image_url ( text ) , lists_users.list_creation_date_utc ( text | values : 2009-12-18 , 2010-01-30 ) , lists_users.list_update_date_utc ( text | values : 2019-11-26 , 2020-05-01 ) ] foreign keys : lists.user_id = lists_users.user_id ratings_users.user_id = lists_users.user_id lists_users.user_id = lists.user_id lists_users.list_id = lists.list_id ratings.user_id = ratings_users.user_id ratings.rating_id = ratings.rating_id ratings.user_id = lists_users.user_id ratings.movie_id = movies.movie_id Matched contents are written in this format table.column (some values can be found in that column) matched contents : movies.director_name ( Christopher Nolan ) movies.movie_title ( The Average , Score ) ratings.critic ( average , AVERAGE , Christopher! , score , Nolan. ) lists.list_title ( CHRISTOPHER NOLAN , christopher nolan , Christopher nolan , Christopher Nolan , Christopher NOLAN , Directed By , average , rating , Nolan , Numbers , Score , movies ) Question: What is the average popularity of each movie that was directed by Christopher Nolan? Indicate which movie directed by him has received the highest number of 5 rating scores. SQL query: SELECT avg(movies.movie_popularity), movies.movie_title FROM ratings INNER JOIN movies ON ratings.movie_id = movies.movie_id WHERE movies.director_name = 'Christopher Nolan' GROUP BY movies.movie_title ORDER BY count(ratings.rating_score) DESC LIMIT 1 Feedback: SELECT. 1. Based on the SQL query, the SQL query selects: [movies.movie_popularity, movies.movie_title] 2. The question asks for ['the average popularity of each movie'] 3. Based on the question, the query should select: [movies.movie_popularity] 4. Compare 1. and 3., the SQL query selects unnecessary columns [movies.movie_title]. 5. Conclude: incorrect. FIXED SQL: SELECT avg(movies.movie_popularity) FROM ratings INNER JOIN movies ON ratings.movie_id = movies.movie_id WHERE movies.director_name = 'Christopher Nolan' GROUP BY movies.movie_title ORDER BY count(ratings.rating_score) DESC LIMIT 1 ========= database schema : table movies , columns = [ movies.director_name ( text | values : Esteban Sapir , Oskar Roehler ) , movies.movie_popularity ( integer | values : 105 , 23 ) , movies.movie_release_year ( integer | values : 2007 , 2006 ) , movies.movie_title ( text | values : La Antena , Elementary Particles ) , movies.movie_id ( integer | primary key | values : 1 , 2 ) , movies.director_id ( text | values : 131 , 73 ) , movies.movie_url ( text ) , movies.director_url ( text ) , movies.movie_title_language ( text | values : en ) , movies.movie_image_url ( text ) ] table lists , columns = [ lists.list_title ( text | values : Headscratchers ) , lists.list_movie_number ( integer | values : 5 , 3 ) , lists.list_description ( text ) , lists.list_id ( integer | primary key | values : 1 , 2 ) , lists.user_id ( integer | values : 88260493 , 45204418 ) , lists.list_url ( text ) , lists.list_comments ( integer | values : 3 , 2 ) , lists.list_followers ( integer | values : 5 , 1 ) , lists.list_third_image_url ( text ) , lists.list_second_image_url ( text ) ] table ratings_users , columns = [ ratings_users.user_id ( integer | values : 41579158 , 68654088 ) , ratings_users.user_trialist ( integer | values : 0 , 1 ) , ratings_users.user_subscriber ( integer | values : 0 , 1 ) , ratings_users.user_has_payment_method ( integer | values : 0 , 1 ) , ratings_users.user_eligible_for_trial ( integer | values : 1 , 0 ) , ratings_users.user_cover_image_url ( text ) , ratings_users.rating_date_utc ( text | values : 2017-06-10 , 2012-10-02 ) , ratings_users.user_avatar_image_url ( text ) ] table lists_users , columns = [ lists_users.list_id ( integer | primary key | values : 192287 , 192313 ) , lists_users.user_id ( integer | primary key | values : 2385 , 15264 ) , lists_users.user_trialist ( integer | values : 1 , 0 ) , lists_users.user_subscriber ( integer | values : 1 , 0 ) , lists_users.user_has_payment_method ( text | values : 1 , 0 ) , lists_users.user_avatar_image_url ( text ) , lists_users.user_eligible_for_trial ( text | values : 0 , 1 ) , lists_users.user_cover_image_url ( text ) , lists_users.list_update_date_utc ( text | values : 2019-11-26 , 2020-05-01 ) , lists_users.list_creation_date_utc ( text | values : 2009-12-18 , 2010-01-30 ) ] table ratings , columns = [ ratings.movie_id ( integer | values : 1066 , 1067 ) , ratings.critic ( text ) , ratings.rating_id ( integer | values : 15610495 , 10704606 ) , ratings.user_id ( integer | values : 41579158 , 85981819 ) , ratings.rating_score ( integer | values : 3 , 2 ) , ratings.critic_likes ( integer | values : 0 , 1 ) , ratings.rating_url ( text ) , ratings.critic_comments ( integer | values : 0 , 2 ) , ratings.user_trialist ( integer | values : 0 , 1 ) , ratings.user_subscriber ( integer | values : 0 , 1 ) ] foreign keys : lists.user_id = lists_users.user_id ratings_users.user_id = lists_users.user_id lists_users.user_id = lists.user_id lists_users.list_id = lists.list_id ratings.user_id = ratings_users.user_id ratings.rating_id = ratings.rating_id ratings.user_id = lists_users.user_id ratings.movie_id = movies.movie_id Matched contents are written in this format table.column (some values can be found in that column) matched contents : movies.director_name ( Steven Spielberg ) movies.movie_release_year ( 2021 ) movies.movie_title ( Spielberg , Release ) movies.movie_id ( 2021 ) lists.list_title ( STEVEN SPIELBERG , steven spielberg , Steven spielberg , Directed By , spielberg , 2021 , Movies released in 2012 , Spielberg! , Released in 2012 , movies! ) ratings.movie_id ( 2021 ) ratings.rating_id ( 2021 ) Question: What are the movie popularity of the movies released in 2021 that were directed by Steven Spielberg? List the names of the movies and their corresponding popularity. SQL query: SELECT movie_popularity, movie_title FROM movies WHERE movie_release_year = 2021 AND director_name = 'Steven Spielberg' Feedback: SELECT. 1. Based on the SQL query, the query selects: [movie_popularity, movie_title] 2. The question asks for ['names of the movies', 'their corresponding popularity'] 3. Based on the question, the query should select: [movie_title, movie_popularity] 4. Compare 1. and 3., The SQL query selects correct columns but in wrong order. 5. Conclude: incorrect. FIXED SQL: SELECT movie_title, movie_popularity FROM movies WHERE movie_release_year = 2021 AND director_name = 'Steven Spielberg' ========= database schema : table master , columns = [ master.firstnhl ( text | comment : first nhl season | values : 1997 , 1943 ) , master.birthcountry ( text | values : Finland , Canada ) , master.playerid ( text | values : aaltoan01 , abbeybr01 ) , master.namegiven ( text | values : Antti , Bruce ) , master.lastname ( text | values : Aalto , Abbey ) , master.birthyear ( text | values : 1975 , 1951 ) , master.namenick ( text | comment : nickname | values : Preacher , Taffy ) , master.firstname ( text | values : Antti , Bruce ) , master.lastnhl ( text | comment : last nhl season | values : 2000 , 1943 ) , master.birthday ( text | values : 4 , 18 ) ] table scoring , columns = [ scoring.playerid ( text | values : aaltoan01 , abbeybr01 ) , scoring.g ( integer | comment : goals | values : 0 , 3 ) , scoring.tmid ( text | comment : team id | values : ANA , CIN ) , scoring.year ( integer | values : 1997 , 1998 ) , scoring.lgid ( text | comment : league id | values : NHL , WHA ) , scoring.gp ( integer | comment : game played | values : 3 , 73 ) , scoring.pos ( text | comment : position | values : C , D ) , scoring.stint ( integer | values : 1 , 2 ) , scoring.pts ( integer | comment : points | values : 0 , 8 ) , scoring.gwg ( text | comment : game-winning goals | values : 0 , 1 ) ] table teamshalf , columns = [ teamshalf.g ( integer | comment : games | values : 10 , 4 ) , teamshalf.year ( integer | primary key | values : 1916 , 1917 ) , teamshalf.tmid ( text | primary key | comment : team id | values : MOC , MOW ) , teamshalf.lgid ( text | comment : league id | values : NHA , NHL ) , teamshalf.rank ( integer | values : 1 , 3 ) , teamshalf.half ( integer | primary key | values : 1 , 2 ) , teamshalf.w ( integer | comment : wins | values : 7 , 3 ) , teamshalf.gf ( integer | comment : goals for | values : 58 , 31 ) , teamshalf.l ( integer | comment : loses | values : 3 , 7 ) , teamshalf.t ( integer | comment : ties | values : 0 ) ] table scoringsc , columns = [ scoringsc.playerid ( text | values : adamsbi01 , adamsja01 ) , scoringsc.tmid ( text | comment : team id | values : VML , CAT ) , scoringsc.year ( integer | values : 1920 , 1921 ) , scoringsc.g ( integer | comment : goals | values : 0 , 2 ) , scoringsc.lgid ( text | comment : league id | values : PCHA , WCHL ) , scoringsc.gp ( integer | comment : games played | values : 4 , 5 ) , scoringsc.pts ( integer | comment : points | values : 0 , 3 ) , scoringsc.pos ( text | comment : position | values : R , C ) , scoringsc.a ( integer | comment : assists | values : 0 , 1 ) , scoringsc.pim ( integer | comment : penalty minutes | values : 0 , 6 ) ] table scoringshootout , columns = [ scoringshootout.playerid ( text | values : adamske01 , afanadm01 ) , scoringshootout.tmid ( text | comment : team id | values : PHO , TBL ) , scoringshootout.g ( integer | comment : goals | values : 0 , 1 ) , scoringshootout.year ( integer | values : 2006 , 2005 ) , scoringshootout.stint ( integer | values : 1 , 2 ) , scoringshootout.gdg ( integer | comment : game deciding goals | values : 0 , 1 ) , scoringshootout.s ( integer | comment : shots | values : 1 , 2 ) ] table teamssc , columns = [ teamssc.g ( integer | comment : games | values : 3 , 5 ) , teamssc.tmid ( text | primary key | comment : team id | values : QU1 , VA1 ) , teamssc.year ( integer | primary key | values : 1912 , 1913 ) , teamssc.lgid ( text | comment : league id | values : NHA , PCHA ) , teamssc.gf ( integer | comment : goals for | values : 12 , 16 ) , teamssc.w ( integer | comment : wins | values : 1 , 2 ) , teamssc.ga ( integer | comment : goals against | values : 16 , 12 ) , teamssc.t ( integer | comment : ties | values : 0 , 1 ) , teamssc.l ( integer | comment : loses | values : 2 , 1 ) , teamssc.pim ( text | comment : penalty minutes | values : 24 , 20 ) ] foreign keys : scoring.playerid = master.playerid scoringsc.playerid = master.playerid scoringshootout.playerid = master.playerid Matched contents are written in this format table.column (some values can be found in that column) matched contents : master.birthcountry ( Canada ) master.namenick ( Mean ) Question: How many Canadian players, between the ages of 18 and 24 when they initially played their first NHL, had a cumulative goal total of no more than 5? Indicate their complete names, the year, and the team for which they scored the specified amount of goals. SQL query: SELECT master.firstname, master.lastname, scoring.tmid, scoring.year FROM master INNER JOIN scoring ON master.playerid = scoring.playerid WHERE master.birthcountry = 'Canada' AND (scoring.g < 5 OR (scoring.g = 5 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) < 18 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) > 24)) GROUP BY master.playerid, master.firstname, master.lastname, scoring.year, scoring.tmid Feedback: SELECT. 1. Based on the SQL query, the query selects: [master.firstname, master.lastname, scoring.tmid, scoring.year] 2. The question asks for ['complete names', 'year', 'team'] 3. Based on the question, the query should select: [master.firstname, master.lastname, scoring.year, scoring.tmid] 4. Compare 1. and 3., The SQL query selects correct columns but in wrong order. 5. Conclude: incorrect. FIXED SQL: SELECT master.firstname, master.lastname, scoring.year, scoring.tmid FROM master INNER JOIN scoring ON master.playerid = scoring.playerid WHERE master.birthcountry = 'Canada' AND (scoring.g < 5 OR (scoring.g = 5 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) < 18 AND strftime('%Y', scoring.year) - strftime('%Y', master.birthyear) > 24)) GROUP BY master.playerid, master.firstname, master.lastname, scoring.year, scoring.tmid ========= database schema : table salesorderheader , columns = [ salesorderheader.territoryid ( integer | values : 5 , 6 ) , salesorderheader.salesorderid ( integer | primary key | values : 71821 , 44088 ) , salesorderheader.salespersonid ( integer | values : 279 , 282 ) , salesorderheader.salesordernumber ( text | values : SO43659 , SO43660 ) , salesorderheader.customerid ( integer | values : 29825 , 29672 ) , salesorderheader.status ( integer | values : 5 ) , salesorderheader.comment ( text ) , salesorderheader.freight ( real | values : 616.0984 , 38.8276 ) , salesorderheader.purchaseordernumber ( text | values : PO522145787 , PO18850127500 ) , salesorderheader.taxamt ( real | comment : tax amount | values : 1971.5149 , 124.2483 ) ] table salesperson , columns = [ salesperson.saleslastyear ( real | values : 0.0 , 1750406.4785 ) , salesperson.salesytd ( real | comment : sales year to date | values : 559697.5639 , 3763178.1787 ) , salesperson.territoryid ( integer | values : 2 , 4 ) , salesperson.businessentityid ( integer | primary key | values : 287 , 275 ) , salesperson.commissionpct ( real | comment : commission percentage | values : 0.0 , 0.012 ) , salesperson.salesquota ( real | values : 300000.0 , 250000.0 ) , salesperson.rowguid ( text ) , salesperson.modifieddate ( datetime | values : 2010-12-28 00:00:00.0 , 2011-05-24 00:00:00.0 ) , salesperson.bonus ( real | values : 0.0 , 4100.0 ) ] table salesterritory , columns = [ salesterritory.saleslastyear ( real | values : 3298694.4938 , 3607148.9371 ) , salesterritory.salesytd ( real | comment : sales year to date | values : 7887186.7882 , 2402176.8476 ) , salesterritory.countryregioncode ( text | values : US , CA ) , salesterritory.name ( text | values : Australia , Canada ) , salesterritory.territoryid ( integer | primary key | values : 2 , 10 ) , salesterritory.costytd ( real | comment : cost year to date | values : 0.0 ) , salesterritory.group ( text | values : North America , Europe ) , salesterritory.costlastyear ( real | values : 0.0 ) , salesterritory.modifieddate ( datetime | values : 2008-04-30 00:00:00.0 ) , salesterritory.rowguid ( text ) ] table stateprovince , columns = [ stateprovince.territoryid ( integer | values : 6 , 1 ) , stateprovince.countryregioncode ( text | values : FR , CA ) , stateprovince.name ( text | values : Ain , Aisne ) , stateprovince.stateprovinceid ( integer | primary key | values : 103 , 101 ) , stateprovince.stateprovincecode ( text | values : 01 , 02 ) , stateprovince.isonlystateprovinceflag ( integer | values : 0 , 1 ) , stateprovince.rowguid ( text ) , stateprovince.modifieddate ( datetime | values : 2014-02-08 10:17:21.0 , 2008-04-30 00:00:00.0 ) ] table customer , columns = [ customer.territoryid ( integer ) , customer.personid ( integer ) , customer.storeid ( integer ) , customer.customerid ( integer | primary key ) , customer.rowguid ( text ) , customer.accountnumber ( text ) , customer.modifieddate ( datetime ) ] table address , columns = [ address.addressid ( integer | primary key | values : 18089 , 23192 ) , address.city ( text | values : Ottawa , Burnaby ) , address.postalcode ( text | values : K4B 1S2 , V5A 4X1 ) , address.stateprovinceid ( integer | values : 57 , 7 ) , address.addressline1 ( text | values : #500-75 O'Connor Street , #9900 2700 Production Way ) , address.rowguid ( text ) , address.addressline2 ( text | values : Space 55 , Unit B-105 ) , address.spatiallocation ( text ) , address.modifieddate ( datetime | values : 2007-12-04 00:00:00.0 , 2008-11-30 00:00:00.0 ) ] foreign keys : salesperson.territoryid = salesterritory.territoryid customer.territoryid = salesterritory.territoryid Matched contents are written in this format table.column (some values can be found in that column) matched contents : None Question: Which territory has the greatest difference in sales from previous year to this year? Indicate the difference, as well as the name and country of the region. SQL query: SELECT saleslastyear - salesytd, countryregioncode, name FROM salesterritory ORDER BY saleslastyear - salesytd DESC LIMIT 1 Execution response [written in pandas format]: 0 1 2 0 1.386404e+06 Southeast US Feedback: SELECT. 1. Based on the SQL query, the query selects: [saleslastyear - salesytd, countryregioncode, name] 2. The question asks for ['difference in sales', 'name of the region', 'country of the region'] 3. Based on the question, the query should select: [saleslastyear - salesytd, name, countryregioncode] 4. Compare 1. and 3., The SQL query selects correct columns but in wrong order. 5. Conclude: incorrect. FIXED SQL: SELECT saleslastyear - salesytd, name, countryregioncode FROM salesterritory ORDER BY saleslastyear - salesytd DESC LIMIT 1