# 404 final

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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

Reporting:

Conclusions stretch beyond scope of work

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
• 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”

• “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”

• “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
are:
• “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
design?
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?
8
18. For 2nd
order multi regression equation for 2 independent variables, how many terms
will be in equation considering all effects?
2
19. For a 3 factor
experiment, how many significance tests will be required to do ANOVA?
7=3!+1
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
Semi-log
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
Sequential
27. An efficient design
of an experiment results in
“elimination of extraneous variables”

& “minimum uncertainty”

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
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
determine
• 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