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Quiz General knowledge
on 30 Mar 12, enabled by
(58% of success)
11 questions - 5 players
An experiment is performed 3 times. Each time a model is fit. This means we generate how many slopes? How many y-intercepts?
1 intercept, 1 slope
3 intercepts, 3 slopes
Using the question above, is it accurate to say that the slope and the y-intercept are both random variables? That is, they vary as we repeat our experiment and fit the model again and again?
No. . they're the same every time
Yes. . each experiment results in its own model. . they're similar though
When we fit a model, the line gives what exactly?
Tells the average/mean response for a change of 1 unit along regressor
Tells us something else
Tells us the exact value of each individual measurement from original data
The picture below shows what happens when we fit a model. Is this true or false?
True. . for each x, we can generate many y's, which are normally distributed
False. . this picture doesn't show what happens
It looks as though the two variables are col-linear. Which means what exactly?
As one varies somehow, the other also varies. . so the two are bound. . so the response shows this
Nothing. . they don't vary together. . the response reflects this independence
The hypothesis stated below are correct for a modeling using a multiple regressors.
The table shown below is correct. That is, it shows the correct entries for multiple regression. n-p=n-(k+1)=n-k-1 k is number of predictors
Prediction intervals are wider then confidence intervals because they depend on the current estimate of the mean and the spread of the data. That is, we fit a model and reach in a grab a new datapoint
If I conduct a regression analysis, and compute t stats. for each regression coefficient, then the square of these is what?
It's the F value found from a partial regression test.
It's something else. .
It's possible to conduct partial regression tests on the contribution made by several predictors, given that several others are already in the model.
This is false.
This is true.
I fit a model first with 1 regressor and then 2. R^2 adjusted increases. This means what?
Nothing. . both are equally good
This means the second regressor has reduced mean sqaure for error,
It means something else
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