Linear Regression

In linear regression the outcome variable is

• Categorical

• Continuous

• Binary

• Nominal

In a regression model the coefficients for each variable is denoted by

• Beta

• Y

• Alpha

• X

Which metrics use absolute value?

• R square

• MAPE

• RMSE

• MPE

Which metric cancels out the negative errors?

• MAE

• RMSE

• MPE

• All of the above

What is multicollinearity?

• Too many variables

• High value for one variable

• Two variables that are highly correlated

• Variable measures are different from each other

Which subset selection method uses very high computer power?

• Exhaustive

• Forward

• Backward

• Stepwise

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

We choose a model which has the highest AIC.

• True

• False