What is the difference between regression analysis and response surface analysis?
When should one use response surface?

  • 1
    $\begingroup$ @Andy: The question is about Response survey analysis too. that's why I included tag which is broader subject that contains this analysis. I searched for "response-survey-analysis" to tag but I did not get that tag hence I added "experiment-design". Could you please enlighten me why it is not suitable tag? Thanks ! $\endgroup$
    – Learner
    Feb 17, 2015 at 16:46
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    $\begingroup$ @Sven Hohenstein, Could you please let me know response surface analysis comes under which broader subject? I'm using Minitab software wherein it has RS Analysis under DOE. This is one of the reason why I went ahead and tagged as "experiment design/DOE". I am curious to know under which category this analysis come in? Thanks ! $\endgroup$
    – Learner
    Feb 18, 2015 at 4:26

1 Answer 1


I'm assuming that you are asking about Multiple regression method and Response surface method. Below is the simple explanation about both methods and their applications. As you read through, you will understand the difference between these two methods.

Multiple regression:

Regression analysis is used to investigate and model the relationship between a response variable and one or more predictors

Multiple regression method may be employed to

  1. find the relationship between factors and the response variable(s)
  2. model a relationship between the response and continuous and categorical variable etc..

Response surface method:

Response surface methods are used to examine the relationship between a response variable and a set of experimental variables or factors. These methods are often employed after you have identified a "vital few" controllable factors and you want to find the factor settings that optimize the response. Designs of this type are usually chosen when you suspect curvature in the response surface.

Response surface methods may be employed to

  1. find factor settings (operating conditions) that produce the "best" response
  2. find factor settings that satisfy operating or process specifications
  3. identify new operating conditions that produce demonstrated improvement in product
  4. quality over the quality achieved by current conditions
  5. model a relationship between the response and continuous and categorical factors

Hope this helps !


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