Introduction To Design And Analysis Of Experiments

Introduction To Design And Analysis Of Experiments – Course objectives and assumptions. A Brief History of the DOE. Strategy of experimentation. Some basic principles and terminology. Instructions for planning, conducting and analyzing experiments. D. Ark. DOE 2 course.

A STAT306 freshman who has heard of normal distribution, knows about mean and variance, done or heard of regression analysis, knows or heard of ANOVA, has knowledge of using SPSS, has not heard of fractional, factorial design, etc. Arku Doe 3 course

Introduction To Design And Analysis Of Experiments

Sir Ronald A. Fisher – Pioneer invented analysis of variance and applied statistics to the design of experiments while working at the Rothamsted Agricultural Experiment Station, London, England. George E. P. Box – married, Fisher daughter still active (age 86) developed response surface methodology (1951) and many other contributions to statistics S44 others Raymond Myers, JS Hunter, WG Hunter, Yates, Montgomery, Finney, et al.. DOE Course 4

Randomized Block Design: An Introduction

RA Fisher and colleagues Significant impact on agricultural science Factorial design, ANOVA First industrial era, 1951 – late 1970s Box & Wilson, Application of response surfaces in the chemical and processing industries in many companies. , beginning around 1990. Widespread Use of Computer Technology in DOE Widespread Use of DOE in Six Sigma and Commercial Use of DOE in Computer Experiments by D. Arcue DOE Course 5

6 References DG Montgomery (2008): Design and Analysis of Experiments, 7th edition, John Wiley and Sons, one of the best books on the market. Uses Design-Expert software for illustrations. Use letters for reasons. GEP Box, WG Hunter and JS Hunter (2005): Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, John Wiley and Sons. A classic 2nd edition text with many examples. No computer solutions. Uses numbers for reasons. Journal of Quality Technology, Technometrics, American Statistics, Discipline Specific Journals D. Arku DOE Course 6

An experiment is a test or series of tests in which purposeful changes are made to the input variables or factors of a system so that we can observe and determine the causes of changes in the output response(s). Question: 5 factors and 2 response variables want to know the effect of each factor on the response and how the factors might interact with each other to predict responses for given factor levels. Reactions – e.g. Maximize Y1 but minimize Y2 Time and budget allocated for only 30 test runs. DOE 7 course

The best guess (trial and error) approach can go on indefinitely There is no guarantee that the best solution will be found. One factor at a time approach Inefficient (requires many test runs) Does not consider any possible interaction between factors. Factor approach (invented in the 1920s) Factors change together. The correct, modern and most effective approach can determine how factors interact, widely used in industrial research and development, as well as for process improvement. DOE 8 course

A Kriging Based Approach To Autonomous Experimentation With Applications To X Ray Scattering

2-level full factorial (2k), fractional factorial (2k-p) and response surface methodology (RSM) DOE are all based on the same statistical principles and method of analysis – ANOVA and regression analysis. DOE 9 course

All experiments should be designed experiments. Unfortunately, some experiments are poorly designed—valuable resources are used inefficiently and the results are inconclusive. Statistically designed experiments ensure efficiency and economy, and the use of statistical methods in the study of data ensures scientific objectivity in drawing conclusions. DOE 10 course

DOE allows experimentation to develop a mathematical model that predicts how input variables interact to produce output variables or responses in a process or system. DOE can be used for a wide range of experiments for different purposes, including almost all fields of engineering and even in business marketing. DOE 11 course

Learn about the process we are exploring. View important variables for creating a mathematical model. DOE 12 course

Design Of Experiments

Experiments are conducted in engineering to: evaluate and compare basic design configurations evaluate different materials select design parameters so that the design performs well in a variety of field conditions (robust design) identify key design parameters that affect performance Course 13 DOE

1. Definition of objectives in the form of specific questions and design structure 2. Selection of treatments to answer the questions 3. Selection of experimental units and replication size 4. Control of variability between sets of units using blocking systems or using auxiliary information (ie, covariate information), collected in units 5. Allocation of treatment in separate units (randomization) Course DOE 14

6. Collection of data that meets the objectives of the study.

17 The terminology allows for clarification of treatment terms, factors, and levels, considering an experiment to evaluate 24 cowpea cultivars and an experiment to evaluate 8 cultivars at three different fertility levels. Course 17

The Basics Of An Experiment

The first experiment has one factor, variety, which has 24 levels (treatments and treatment levels do not differ, the second has two factors, variety, which has 8 levels, and fertility has 3 levels (24 different combinations/treatments, the third has three factors) 4, 3 and 2 levels, respectively. Includes 24 procedures. DOE 18 course

19 Terminology Experimental units are things to which we apply treatments, such as a plot of land, a group of customers, etc. Treatments are different procedures that we want to compare. Example. Experimental error is a random variation of all experimental results; Different experimental units will give different responses to the same treatment. Or applying the same treatment over and over to the same device will result in different reactions. This does not mean a false experiment. The reasons are related to the form of treatment. The parameters for each factor are called DOE 19 course levels

20 Confusion with terminology – occurs when the effect of one factor or treatment cannot be distinguished from the effect of another factor or treatment. They say that the two causes or treatments are confused. Example. Consider planting grade A corn in GT and grade B corn in Cape Coast. In this case, we cannot distinguish location effects from cross effects. The diversity factor and location factor are confused, DOE 20 course

Photography Factors: Film Speed, Lighting, Shutter Speed ​​Answer: Quality of Flash Close-up Slides Boiling Factors: Pot Type, Burner Size, Lid Answer: Water Boiling Time D-Day Factors: Beverage Type, Quantity Beverage, Speed consumption, time since the last meal. Answer: time to guide the steel ball through the maze. Shipping factors: brand, city code, time of day when the letter was sent. Answer: the number of days it takes to deliver a letter. DOE 21 course

Theory Guided Experimental Design In Battery Materials Research

Factors: Amount of cooking wine, oyster sauce, sesame oil Answer: The taste of stewed chicken Basketball factors: Distance from the basket, type of shot, location on the floor Answer: Number of shots (out of 10) with a basketball Skiing factors: type of ski, temperature , type of paraffin Answer: Time to ski down DOE Course 22

The process of designing experiments so that relevant data can be analyzed using statistical methods that lead to valid, objective, and meaningful conclusions based on the data involves two aspects: planning and statistical analysis. Course 23 DOE.

A hypothesis is a hypothesis that prompts an experiment. Experiment – testing is done for research. Hypothesis analysis — statistical analysis of experimental data. The conclusion is what was learned about the initial hypothesis of the experiment. DOE 24 course

Replication allows for estimation of experimental error Allows for more accurate estimation of sample mean Randomization of value Keystone of all statistical methods “Averaging” Influence of extraneous factors Reduces bias and systematic errors Blocking increases precision of experiment “Remove” variable not studied Course DOE 25

Pdf) Conjoint Analysis And Discrete Choice Experiments For Quality Improvement

Researchers use experiments to answer questions. Typical questions might be: Is the drug a safe and effective treatment for the disease? This could be a test of how AZT affects the development of AIDS. DOE 26 course

Does short-term incarceration of an abusive spouse deter future assaults. How might mobile usage change if our company offers a different rate structure to our customers, DOE Course 27?

This allows us to make direct comparisons between the treatments of interest. We can see the differences in the experiment. In particular, we can draw conclusions about the reason for the DOE 28 course

29 Why experiment? Mosteller and Turkey (1997) list three

Ship Collision Damage Assessment And Validation With Experiments And Numerical Simulations

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