Historical design refers to the use of past experimental data to build a response surface model.
This approach leverages previously collected data, which can be particularly useful when conducting new experiments is not feasible due to constraints like time, cost, or material availability.
Advantages of Historical Design:
Cost-effective, as it utilizes existing data.
Can provide a broad understanding if data covers a wide range of factor levels.
Challenges:
The data may not be perfectly suitable for RSM if it lacks sufficient variation or replication.
Potential biases and inconsistencies in historical data.