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- 1. Multiple Choice
In linear regression the outcome variable is
Categorical
Continuous
Binary
Nominal
- 2. Multiple Choice
In a regression model the coefficients for each variable is denoted by
Beta
Y
Alpha
X
- 3. Multiple Choice
Which metrics use absolute value?
R square
MAPE
RMSE
MPE
- 4. Multiple Choice
Which metric cancels out the negative errors?
MAE
RMSE
MPE
All of the above
- 5. Multiple Choice
What is multicollinearity?
Too many variables
High value for one variable
Two variables that are highly correlated
Variable measures are different from each other
- 6. Multiple Choice
Which subset selection method uses very high computer power?
Exhaustive
Forward
Backward
Stepwise
- 7. Multiple Choice
Why do we use adjusted R square?
To adjust the initial R square result
To penalize the addition of too many variables
To make the model reliable
To compare between training and validation datasets
- 8. Multiple Choice
We choose a model which has the highest AIC.
True
False
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