Part A.

Match the situation with the appropriate forecast error metrics at the bottom: a. You want to know if the forecast method is biased or not.

b. You have a negative forecast error.

c. You want to compare two different applications of the same forecasting method.

d. You want to emphasize large errors.

options:

*mean absolute percentage error

*mean forecast error

*coefficient of determination

*sum of squared errors

Part B.

Match the description with the concept it illustrates at the bottom: a. It is used to predict the outcome of a variable of interest based on the value of one variable.

b. It is the variable of interest being predicted.

c. It uses an equation to capture the relationship between variables.

d. It is used to minimize the error sum of squares to determine the parameters of the regression equation.

e. It is used to predict the outcome of a variable of interest based on the value of a categorical variable.

Options:

*Partial Least Squares method

*Regression Modeling

*Multiple Regression

*Dependent Variable

*Simple Regression

*Independent Variable

*Ordinary Least Square method