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A designed experiment can help you choose between alternatives and select key factors affecting a response even despite uncontrollable noise factors. You can also use Response Surface modeling to hit a certain target, reduce variability in a process, maximize or minimize a response, make a process more robust despite uncontrollable noise and even pursue multiple goals.

## Basic steps of DOE

- Define objective(s)
- Gather knowledge about the process
- Develop a list and select your variables
- Assign levels to variables
- Conduct experiments
- Data analysis and conclusions

## Items to avoid when conducting the experiment:

- Unwarranted assumptions of the process.
- Undesirable combinations of the factors.
- Violation of known laws of physics.
- Too large or small design sizes.
- Inappropriate confounding.
- Imprecise measurement.
- Unacceptable prediction error (Type 1 & type 2 errors).
- Undesirable run order.

## Introduction to Design of Experiments Video

## Planning and Organizing Designed Experiments

- Define interactions
- Understand how to analyze interactions

**Balanced experiment**: each factor appears the same number of times.

**Full factorial experiment:** At least one trial for all possible combinations of factors and levels

## One Factor at a Time (OFAT)

Explain the one factor at a time (OFAT) approach

### Randomized block plans

### Latin square designs

## Experimental Notation

Experiments can be abbreviated numerically.

### Design Ex.

2^5 means that there are 5 factors at 2 levels.

### Fractional Design Ex.

2^5-2 means that there are 5 factors at 2 levels and 2 generators. The generator determines what effects are confounded or combined with one another.

We would call this example an 1/L^g fractional factorial.

Thus, 2^5-2 is a 2 level, 5-factor, 1/4th fractional design.

## Design of Experiments Examples

Add examples.

## Design of Experiments Videos

Good SlideShare presentation:

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