Lecture

Full Factorial Design Introduction

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

MICRO-110: Probability & statistics for engineers

This course covers the basics of probability and statistical techniques for engineers, including major probability concepts, data analysis, and statistical inference.

Description

This lecture introduces the concept of full factorial design, explaining the process of creating experimental designs to study the influence of multiple factors on a response variable. Starting with the basics of factors, levels, and treatment conditions, it progresses to coding the design, graphical outputs, and interpreting the results. The lecture covers the importance of main effects, interactions, and the conservation of degrees of freedom in experimental data analysis.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Vivek Subramanian

Teaching & PhD PhD Students Giovanni Ruffolo, Giada Romano, Sanjay Manoharan, Kyle Haas, Claude Dahan, Biruktait Ayele Lemecho, Yagmur Ceren Alatas, Tugçe Delipinar Past EPFL PhD Students Amin Hodaei, Mustafa Mohieldin Fadlelmula Fadlelseed Past EPFL PhD Students as codirector Mohammad Jafarpour Courses Organic and printed electronics MICRO-505 This course addresses the implementation of organic and printed electronics technologies using large area manufacturing techniques. It will provide

Official source

Related concepts (32)

Factorial experiment

In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.

Fractional factorial design

In statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.

Optimal design

In the design of experiments, optimal designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design.

Graphical widget

A graphical widget (also graphical control element or control) in a graphical user interface is an element of interaction, such as a button or a scroll bar. Controls are software components that a computer user interacts with through direct manipulation to read or edit information about an application. User interface libraries such as Windows Presentation Foundation, Qt, GTK, and Cocoa, contain a collection of controls and the logic to render these.

Design of experiments

The design of experiments (DOE or DOX), also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation.

Related lectures (57)

Design of Experiments: Efficiency and Interactions

Introduces the Design of Experiments, emphasizing efficiency and interactions in R&D through historical context and practical examples.

Introduction to ANOVA

Covers the basics of ANOVA using a case study on sleeping pill effectiveness.

Fractional factorial design: Basics and Applications

Explores fractional factorial design basics, including blocking alias, interaction effects, and design comparison.

Fractional factorial design II

Covers the analysis of a 24-1 fractional factorial design and its aliases.

Factorial Experimental Design