complete the SQL statement below. A table called "tickets" tracks the number of tickets sold each day at a movie theater. (Each row represents a different day.) The statement should create a new column called "tickets_yesterday" that includes the "tickets_sold" from the previous day. Within this statement, in addition to this new column, include the date_id and tickets sold. Sort the data by the date_id in ascending order. #1 #2 #3 SELECT date_id, tickets_sold, LAG ( FROM tickets; ) OVER (ORDER BY ) AS complete the SQL statement below. A table called "sales" tracks revenue by year and quarter. (Each row represents quarterly sales in a given year.) The statement should create a new column called "year_over_year" that includes the "quarterly_sales" from a year earlier. (As an example, quarter 4 sales in 2022 would be compared to quarter 4 sales in 2021.) Within the statement, in addition to this new column, include the year, quarter, and quarterly_sales columns. Sort the data by the year and quarter in ascending order. #1 #2 #3 SELECT year, quarter, quarterly_sales, FROM (quarterly_sales, sales; ) OVER (ORDER BY year, quarter) AS