Quiz Regression1

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.

False

True

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

True

False

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

True

False

If I conduct a regression analysis, and compute t stats. for each regression coefficient, then the square of these is what?

It's nothing.

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

An experiment is performed 3 times. Each time a model is fit. This means we generate how many slopes? How many y-intercepts?

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?

When we fit a model, the line gives what exactly?

The picture below shows what happens when we fit a model. Is this true or false?

It looks as though the two variables are col-linear. Which means what exactly?

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 possible to conduct partial regression tests on the contribution made by several predictors, given that several others are already in the model.

I fit a model first with 1 regressor and then 2. R^2 adjusted increases. This means what?

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