Please answer all of the questions.

**Q1**: The best way to learn which forecasting method is most effective for a given situation is to:

**Q2**: When the aim is forecast accuracy, complex uncertain situations call for complex models.

**Q3**: Tests of statistical significance are useful for choosing which variables to include in a time-series forecasting model

**Q4**: For complex situations about which there is much uncertainty, experts' judgmental forecasts are:

**Q5**: For judgmental forecasting, it is better to obtain forecasts from experts with diverse knowledge and opinions than to obtain forecasts from the expert with the best track record

**Q6**: When forecasters provide 95% prediction intervals for their forecasts, what percent of the time does the interval contain the actual value?

**Q7**: Judgmental bootstrapping involves modeling an expert's forecasting process by regressing the expert's forecasts against the variables that the expert considered. Such models confer which of the following benefits? (Check all that apply.)

**Q8**: Combined (averaged) forecasts from different forecasting methods are more accurate than the typical individual forecast

**Q9**: You should *not* combine forecasts from different methods if you know which method will be best for the type of situation you are concerned with.

**Q10**: For time-series forecasting, the best guarantee of accurate forecasts is a model that closely fits historical data.

**Q11**: For time-series forecasting, when you are uncertain about the situation you should develop a model using

**Q12**: The best way to judge whether a forecasting method can provide accurate forecasts is for practitioners to use it.

**Q13**: When relevant quantitative data are available, proper quantitative forecasting methods provide forecasts that are more accurate than forecasts made using judgmental methods.

**Q14**: On average, the error of a combined forecast is how much smaller than the error of a typical forecast?

**Q15**: When understanding about a situation is tentative, you should extrapolate recent trends to obtain forecasts for longer time periods.

**Q16**: Econometricians' improvements in estimating relationships in non-experimental time-series data mean that they can now make ex ante (unconditional) forecasts that are:

**Q17**: Expert adjustments to forecasts from quantitative models

**Q18**: Data about analogous situations can be used to improve forecast accuracy.

**Q19**: Using a forecasting method that represents the situation realistically increases forecasting accuracy.

**Q20**: Scenarios are useful for