1. Explain the statement
    that an experiment is a model. Note and explain the similarities and
    differences between an experimental model and a theoretical model of the type
    that you have studied exclusively in your courses.
    • Exp model – relationship among parameters w/ physical
    • simulations

    Similarity – both are relationships btwn parameters

    • Differences – Experimental data vs. mathematical
    • relationships to generate model
  2. Explain what it means
    that an experiment should produce results that correlate to performance in the
    real world. Explain how we can attempt to assure that this is the case?
    • Experiments should produce results that closely resemble
    • what happens in the real world; good experiments give results that are similar
    • to field data

    • To ensure similarity btwn experiment data and real world,
    • try to use same conditions, same materials, same correlations
  3. The other side of
    designing a good experiment is to evaluate & critique an experiment. State
    2 aspects of exp design, conduct, or reporting that could be used to attack exp
    validity and explain how they might be used to question results.
    Exp design:

    Too much uncertainty

    Not controlling parameters well

    Confounding variables


    Conclusions stretch beyond scope of work

    Bad data reduction
  4. Explain difference
    and importance: Error & Uncertainty
    • Error - % difference from known true value; provides definite
    • conclusion of “right/wrong”

    • Uncertainty – amount of variation w/ respect to accuracy;
    • confidence of conclusions in exp
  5. Explain difference
    and importance: Findings & Conclusions
    Findings – summary of data trends and observations

    • Conclusions – Answer objectives; statements of understanding
    • that can be applied to predict results of other experiments
  6. Explain difference
    and importance: Objectives & Tasks
    • Objectives – explicit statement of what is to be done,
    • determined, answered, or developed; gives explicit purpose for doing the
    • experiment

    • Tasks – Things that need to be done to accomplish the
    • objectives
  7. Explain difference
    and importance: Goal & Objectives
    Goals – more broad, long term things to be accomplished

    • Objectives – explicit statement of what is to be done,
    • determined, answered, or developed; gives explicit purpose for doing the
    • experiment
  8. Explain difference
    and importance: Correlation & Causation
    • Correlation – Some trend exists between two variables, but
    • may be non-quantifiable; an experiment might be discarded if no correlations
    • exist

    • Causation – Quantifiable proof that one variable has a
    • direct effect on the behavior of another variable; may legitimize an experiment
    • by demonstrating data trends
  9. Explain difference
    and importance: Rectification of results & Normalization of results
    • Rectification – using mathematical manipulation to present
    • data as a straight line

    • Normalization – comparison of data to some baseline to
    • establish a measure of relative difference
  10. Good or Bad Obj: “The
    objective of this report is to compare toaster ovens a,b,c,d”
    Bad; “compare” is too vague.

    • “The objective of this report is to compare the cooking
    • times of toasters a,b,c,d”
  11. Good or Bad Obj: “The
    objective was to measure the time AA batteries last at different loads”
    Bad; “measure” is a task.

    • “The objective of the exp is to determine the rate of electrical
    • discharge of AA batteries at different loads”
  12. Statisticians usually
    define uncertainty using the resulting data for some type of regression
    analysis. This is:
    • “Not useful during planning” & “useful after experiment
    • is completed even if it was a single value”
  13. Extraneous variables
    • “continuous variables which are secondary independent
    • variables”
    • &
    • “variables external to an experiment which may affect the
    • outcome”
  14. The effect of
    extraneous variables may be suppressed by:
    • “controlling the experiment so that extraneous variables
    • have little effect”
    • &
    • “randomizing data taking so that the effect of
    • extraneous variables is spread over the test envelope”
  15. The spacing of data
    points within the test envelope:
    • “should be done to equalize the uncertainty throughout the
    • test envelope”
  16. For a 3 factor
    experiment with 4 levels of factor 1, 3 levels of factor 2, and 2 levels of
    factor 3, what number of tests will be required to conduct a full factorial
    24 = 4*3*2
  17. Consider a 3 factor
    experiment with 4 levels of factor 1, 3 levels of factor 2, and 2 levels of
    factor 3. How many terms will there be in a linear multiple regression equation
    for the result including all effects?
  18. For 2nd
    order multi regression equation for 2 independent variables, how many terms
    will be in equation considering all effects?
  19. For a 3 factor
    experiment, how many significance tests will be required to do ANOVA?
  20. Number of significant
    figures to be used for reporting numerical results should:
    “Convey accuracy of instruments”

    & “be based upon measurement with worst uncertainty”

    & “reflect # of sig figs used for calc”

    & “be based upon uncertainty of value reported”
  21. Uncertainty analysis
    should be used:
    “when reporting results”

    & “during planning of exp”

    & “during the execution of an experiment”
  22. To graphically rectify
    data exhibiting exponential behavior, ___ coord. Should be used
  23. When plotting data
    for pressure drop for fluid flow in duct, where uncertainty is equal throught
    test envelope; the data should be spaced at:
    Equal increments of flow rate (x variable)
  24. When designing an
    experiment one should strive for uncertainty which is:
    Adequate, considering what results will be used for
  25. When designing an
    experiment the amount of data that is planned to be collected should be
    • Just
    • enough to plot a curve
  26. An irreversible
    experiment always requires a ____ plan
  27. An efficient design
    of an experiment results in
    “elimination of extraneous variables”

    & “minimum uncertainty”

    & “adequate useful data”
  28. A random experiment
    design should be used:
    To suppress the effect of extraneous variables
  29. A classical
    experiment plan
    Involves holding one variable constant at a time
  30. Adequate uncertainty
    refers to uncertainty that is:
    Sufficiently low so that the objective can be satisfied
  31. An irreversible
    experiment is one for which:
    Measurements cannot be repeated because of a transition
  32. ANOVA can be used to
    • The confidence level of the significance of the effect of a
    • factor on the response variable
  33. Randomized block
    experiment designs can be used
    To suppress the effect of discrete extraneous variables
  34. Balance equations can
    be used
    • “To check on the reasonableness of data”
    • &
    • “establish
    • criteria for rejecting data”
  35. Questions concerning
    your results should be
    • Anticipated and considered during the design of the
    • experiment
  36. An experiment is
    confounded when
    • The effect of one independent variable on the dependent
    • variable is indistinguishable from the effect of another
Card Set
404 final flashcards