Step 1: Select rows where 'year' is greater than 2003.
year |
regional_gva |
agriculture |
industry |
services |
1995 |
4753 |
11 |
1110 |
3632 |
2000 |
6584 |
10 |
1302 |
5277 |
2003 |
8201 |
11 |
1374 |
6816 |
2005 |
8978 |
11 |
1465 |
7502 |
2007 |
9432 |
11 |
1565 |
7856 |
Step 2: Select rows where 'industry' is less than 1465.
year |
regional_gva |
agriculture |
industry |
services |
2005 |
8978 |
11 |
1465 |
7502 |
2007 |
9432 |
11 |
1565 |
7856 |
Step 3: Select rows where 'regional_gva' is greater than 9432.
year |
regional_gva |
agriculture |
industry |
services |
Step 4: Calculate the average of the 'services' column and add column 'avg_services' to the existing table.
year |
regional_gva |
agriculture |
industry |
services |
Step 5: Select rows where 'avg_services' is less than 7.
year |
regional_gva |
agriculture |
industry |
services |
avg_services |
None |
None |
None |
None |
None |
None |
Step 6: Use a `CASE` statement to return TRUE if the number of rows is equal to the number of rows in the previous step, otherwise return FALSE.
year |
regional_gva |
agriculture |
industry |
services |
avg_services |
Prediction: TRUE