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Introduction to Experimental Design

Fundamentals of Experimentation. Clearly define the objectiveWhat question are you trying to answer? How will you know you are finished?Choose the factor(s) of interest- The response to measure,The data analysis techniquesConsider the design specificsRange- Replication - RepetitionRan

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Introduction to Experimental Design

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    1. Introduction to Experimental Design Engineering Experimental Design Winter 2003

    2. Fundamentals of Experimentation Clearly define the objective What question are you trying to answer? How will you know you are finished? Choose the factor(s) of interest - The response to measure, The data analysis techniques Consider the design specifics Range - Replication - Repetition Randomization - Blocking - Risks Collect the data Record all conditions carefully. Will you understand this later? Analyze the data Graphs & descriptive statistics first - Hypothesis testing & regression next Interpret the results Draw conclusions - Make recommendations

    3. Advantages of Designed Experiments Enable data-based decisions Create understanding of a process and how to control it Take into account the inherent noise in the system Provide maximum information for the amount of effort Detect variable interactions

    4. A Designed Experiment Should . . . Meet the objective Allow for simultaneous study of multiple factors Cover the region of interest Obtain maximum information for minimum cost Be simple to analyze and interpret Enable the experimenter to Distinguish important from unimportant factors Develop a mathematical model Test for model adequacy Estimate experimental uncertainty

    5. Terminology Controlled variables Xs - Factors Treatments - Independent variables Outputs Ys - Responses Effects - Dependent variables

    6. Types of Experimental Designs Factorial To distinguish important from unimportant Xs To form limited models Fractional factorial To make a preliminary distinction between important and unimportant Xs Response Surface To determine the relationship between Ys and the important Xs (develop a model) Regression is a way to analyze data from a response-surface experiment

    7. Fundamentals of Experimentation Clearly define the objective What question are you trying to answer? How will you know you are finished? Choose the factor(s) of interest - The response to measure, The data analysis techniques Consider the design specifics Range - Replication - Repetition Randomization - Blocking - Risks Collect the data Record all conditions carefully. Will you understand this later? Analyze the data Graphs & descriptive statistics first - Hypothesis testing & regression next Interpret the results Draw conclusions - Make recommendations

    8. Design Specifics - Range Cover the region of interest Are you in the right flow regime? Wide enough to see the effect of interest Will the real change in Y be bigger than the variability in Y? For regression, remember that model parameters (adjustable parameters, slope & intercept) can be determined more precisely from a wide range of data than from a narrow range

    9. Design Specifics - Replication Means coming back to the same conditions at a different time Allows you to estimate the inherent noise in the process Allows you to distinguish between a real response and normal variability Allows you to estimate the overall uncertainty in the experiment

    10. Design Specifics - Repetition Provides an estimate of the variability within a given run Measurement uncertainty Variations in water pressure, temperature Provides an opportunity to study the variability in Y for a given X

    11. Repetition and Replication

    12. Replication and Repetition In practice, in an experimental situation, it can be difficult to achieve true replication. Do you really want to spend 30 minutes twiddling the valves to get exactly the same flow rate you got yesterday? How do you decide whether the current conditions are close enough to replication? Consider uncertainty on flow rate.

    13. Design Specifics - Randomization An insurance policy Helps ensure that the effects of unknown, unidentified, or uncontrolled variables do not bias our experiments Helps ensure the validity of statistical assumptions

    14. Design Specifics - Blocking Allows you to see a difference in Y due to one X, in spite of a change in another X How do you use blocking to see the effect of shell-side flow regime on overall heat transfer coefficient, in spite of the effect of tube-side flow regime on overall heat transfer coefficient?

    15. Design Specifics - Risk Not a part of statistics, but something to consider to avoid becoming a statistic Potential danger to people Potential danger to property Potential danger to environment

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