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Quiz General knowledge
Design Of Experiments
on 6 Mar 12, enabled by
(51% of success)
30 questions - 1 349 players
Questions about what we've learned so far.
We can use use ANOVA to test the equality of two or more means.
Huh? . . what's ANOVA?
What is meant by a replication of an experiment?
A complete performance of the experiment. That is, every treatment possibility is applied.
Only some of the treatments are applied.
Does the picture below suggest that we have a significant result for differences between treatments.
True. . it's obvious one of the means must be different
False. . the means all seem to fall within the spread of the data. . it would be difficult to tell them apart.
In a completely randomized experiment all of the runs are made in random order.
Why randomize the application of treatments in the first place?
There is no reason to randomize. We do it because we're told to do it.
Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design.
Sometimes factor that are unknown can play a role, and randomization breaks any dependence between these factors, so their effect is negligible.
1 and 2 options both.
In a fixed effect experiments, the treatments are chosen at random.
The basic anova identity is shown below.
Which is the correct value for the expected value of the mean square of the error?
If the F-statistic in a single-factor ANOVA is significant this indicates that all of the means being compared are different.
According to the data given below, what is the value of the F statistic?
In the picture below, does it seem that the results of the should be significant or not? That is, does it seem that at least one of the treatment means is different from the others?
Yes. . it seems so. .
No. . it doesn't seem so
I don't know. . how do I tell?
In the single-factor ANOVA model, the mean of treatment i is the sum of the grand mean and the ith treatment effect. Is the picture below correct?
In the picture below, what exactly is the factor and what are the treatment levels and the response?
The etch rate is the factor and the levels of etch rate are the response
The power is the factor and the settings of power are the treatment levels and the etch rate is the response
Neither of these is correct
In the picture below, we see one one replicate of the experiment highlited.
True. . one power setting. . several measurements
False. . only 1 power setting is used instead of all
In the picture below, we see one full replicate of the experiment highlited
True. . all the power setting are applied
False. . this picture doesn't show one full replicate
In the picture, how many runs are in the red box? How many deviations from this treatment mean? How many degrees of freedom for error under this treatment?
There is one mean to be calculated for this level . . 5 runs, so 4 degrees of freedom with respect to this treatment level mean
5 runs. . so four deviations from this treatment level mean. . and the 5th is forced
Both of the above are accurate
Neither of answers 1 or 2 works
In the picture on the left, we see 3 runs under level 1 of the factor. This means there is a mean and consequently 2 degrees of freedom for error with respect to this mean. We have 4 all together.
In the picture we see 6 data points, and 1 grand mean. This means we can calculate 5 deviations from this mean the 6 is forced by the values of the first 5. So we have only 5 degrees of freedom.
Is the picture shown on the left correct or incorrent?
In the table below, if I increase n, what happens to the number of dots in a scatter diagram at every treatment level? Do the dots increase in number vertically or horizontally?
It goes down vertically
The dots increase in number. . more appear at every level vertically
Nothing happens. . there is no connection between scatter points and n
If I increase a in the table below, what will happen to the number of dots in a scatter diagram? Will the increase come from adding dots vertically at every level or horizontally WITH new levels?
It will go down
It will go up as we add levels. . horizontally
Which picture, the left or right, shows the correct groupings in terms of the definition of a replicate of an experiment?
If in the table below, I increase n, will the degrees of freedom for error increase or decrease? Is the total variabiilty in the data affected? Are the degrees of freedom for treatments affected?
Freedom degrees for error increase, treatment degrees go down, total variability stays the same
Freedom degrees increaes, treatment degrees are constant, total variability increases
In the table below, if we change the number of dots in the scatter diagram per level from let's, say, 3 to 5, which of the following will happen?
Treatment means will change. . so SSTreatment will change. . and so will the MStreatment. . and so will the F ratio. . total variability will change
Nothing will happen. .
Most of the change will be in the error
Some but not of all the things in the first option will happen
If in the picture below, I change the number of levels from, let's say 2 to 3, what will happen ?
Df for treatments will increase, the grand mean will change, the SStreatments will change, the MSsquaretreatments will change and the F test will chan
Nothing will happen. .
Some of the things in option 1 will occur
Something else will happen
Below is a correctly completed table for one factor randomized experiment.
Below is a correctly completed table for a one factor randomized experiment with blocking.
Why is repeated measures given the name it's given?
Because we run multiple replicates
Because one person, let's say, gets the same treatment multiple times
Some other reason not listed here
If we have 2 factors and two levels per factors, what kind of experiment is this?
None of the above
If we have 2^3 experiment, what does this mean and we run 10 replicates, what's the result?
2 factors, 3 levels per factor, 60 runs
3 factors, 2 levels per factor, 80 runs
It's neither of the above
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