A simple one or two table inquire exercise these performance even when the measurements of brand new numeric ranges aren’t identical. Within example, i fool around with several tables (Age range and you may Customers), and one ask (Frequency: Years Shipment). Merely realize such points:
Merely manage a desk that have five areas: Category ID (counter), Class Title (text), Minimal (number), and Restrict (number). For each and every checklist, identify the fresh organizations and its particular reasonable and you may large beliefs:
Observe how [Maximum] property value one checklist is smaller than the latest [Minimum] value of the next number. They can’t getting the same if not such beliefs do end up in a couple groups. Inside our analogy, the age study try integers therefore 
using integers within table is fine. Otherwise, you can use quantity very close to one another (age.grams. 999). You could title and you can identify as numerous groups as you like.
Step 2: Perform multiple-table Select query
Notice that both dining tables boxes commonly linked to for every most other. The first and you may 3rd industries on the query are from the fresh class table: new [Class Name] description as well as the [Group ID] community controlling the types purchase. Another profession ‘s the number of one’s Patient (data) table’s [ID] field (so it occupation is utilized because it’s maybe not null per record). The final occupation describes the link among them dining tables. Using the Patient table’s [Age] industry, the criteria is actually «Between [Minimum] And you may [Maximum]». This «bins» the patient research towards age groups defined of the Group desk.
Step three: Run this new Query
If your Classification table’s [Category ID] profession isn’t included in new inquire, the outcomes would be found in Ascending purchase by the [Classification Term] («Less than twenty five» will be last checklist).