1. Since experimentations are always subject to errors, what techniques are employed to neutralize the effects of these errors in the accuracy and validity of the experiment?

Elements Of Electromagnetics
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Author:Sadiku, Matthew N. O.
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1. Since experimentations are always subject to errors, what techniques are employed to
neutralize the effects of these errors in the accuracy and validity of the experiment?
2. Why is the "factorial experiment" better in any way than a "one-factor-at-a-time"
approach?
3. True or False: As soon as the experimental data (measurements) are obtained, the
experimenter can already proceed with the usual significance testing. Explain.
4. What is "hypothesis testing"? How is it performed?
5.What makes ANOVA a powerful tool in hypothesis testing?
What happens if you use a 2k full Factorial design to explore a set of variables which is
known to have a non-linear response?
6. When is the addition of center points to a 2k full factorial design beneficial? Give a
particular case that warrants its use.
7. In practical terms, explain how a LOW p-value connote SIGNIFICANCE (Recall that the
smaller the p-value is, the more significant the factor becomes)?
Transcribed Image Text:1. Since experimentations are always subject to errors, what techniques are employed to neutralize the effects of these errors in the accuracy and validity of the experiment? 2. Why is the "factorial experiment" better in any way than a "one-factor-at-a-time" approach? 3. True or False: As soon as the experimental data (measurements) are obtained, the experimenter can already proceed with the usual significance testing. Explain. 4. What is "hypothesis testing"? How is it performed? 5.What makes ANOVA a powerful tool in hypothesis testing? What happens if you use a 2k full Factorial design to explore a set of variables which is known to have a non-linear response? 6. When is the addition of center points to a 2k full factorial design beneficial? Give a particular case that warrants its use. 7. In practical terms, explain how a LOW p-value connote SIGNIFICANCE (Recall that the smaller the p-value is, the more significant the factor becomes)?
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