How to choose between Regression analysis and DOE?

What are diffrent critical factors which contribute in the decision process of above  and how?

Hi Sanjay,

Great question — teams often struggle with when to use Regression and when to use DOE, because both study relationships between inputs and outputs. The choice becomes easy once you look at the purpose, control over factors, and type of insights needed.

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When to Use Regression

Use Regression Analysis when:

• The data is already available (historical, observational, process data)

• You cannot control the input factors (e.g., field data, customer usage data)

• You want to model relationships between variables without disturbing the process

• You are testing for statistical significance, strength of relationship, or prediction

Regression answers:
“Which factors currently correlate with the output and how strong is the relationship?”

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When to Use DOE (Design of Experiments)

Use DOE when:

• You can control the input factors (machine settings, parameters, materials, methods)

• You want to establish causation, not just correlation

• You need to study interactions between variables

• You want to identify the optimal settings for the process

DOE answers:
“What settings of the factors produce the best output, and which interactions matter most?”

Simple Rule of Thumb

• If you can experiment → choose DOE.

• If you cannot experiment and only have data → choose Regression.