Forecasting techniques increase in accuracy for longer time periods into the future.

Is this true or False?

Which is NOT true about the adjusted R-Square?

It is not useful when we add variables to a regression model.

It is useful for comparing regression models with a different number of independent variables.

It incorporates the number of explanatory variables.

It is a modified R-Square value.

When an independent variable is significant in multiple regression it is proven to have a specific association with the dependent variable.

Is it True or False?

What is NOT considered in an exponential smoothing model?

class

level

trend

seasonality

A smoothing constant closer to 0 puts more weight on the prior forecast, making the model respond slower to changes.

Is it True or False

Which error measure is useful in cases where the linear regression assumptions are true?

Tracking Signal Error

Mean Square Error

Signal Error

Mean Forecast Error

What makes a good regression model?

significant independent variables

including the largest possible number of variables

dropping all insignificant variables from the model

a significant intercept and dependent variable

Which type of exponential smoothing technique is best for data with no trend or seasonality?

Triple Exponential Smoothing

Double Exponential Smoothing

Single Exponential Smoothing

Holt-Winters Exponential Smoothing

What type of model can be used for data with seasonality?

Multiple Regression and Holt-Winters

Single and Double Exponential

Holt-Winters ONLY

Multiple Regression ONLY

Which error measure can tell us whether there is a bias in the forecast model?

Mean Absolute Error

Tracking Signal Error

Root Mean Square Error

Mean Square Error