Forecasting

Which of the following is not an element of a good forecast?

• A good forecast must be timely, allowing decisions to be made before critical events occur.

• A good forecast must be accurate, even if it's based entirely on personal judgment.

• A good forecast must be reliable, demonstrating consistent performance over time.

• A good forecast must be meaningful, offering insight that is clearly understood.

In time series data, which of the following best describes random variation?

• This refers to small, irregular fluctuations in data that are unpredictable and not caused by any identifiable factor.

• This represents long-term upward or downward movement in the data driven by overall market behavior.

• This involves regular, repeating patterns that occur at consistent intervals such as quarters or months.

• This occurs due to cyclical movements caused by economic expansions and contractions over several years.

Which of the following real-life events is most likely to cause irregular variation in a time series?

• A steady increase in housing prices due to inflation over the past decade

• An annual surge in toy sales during the holiday season

• A sudden earthquake that disrupts supply chains and production

• A recurring dip in sales every February due to post-holiday consumer fatigue

The following defines time series data, except:

• The data may include metrics such as demand, unit sales, or profits

• Observations recorded hourly, daily, weekly, or monthly

• A random collection of data points taken at any point in time

• All of these define time series data

These do not define seasonality, except:

• Irregular, one-time spikes in sales due to rare events

• Recurring peaks and troughs patterns that repeat at fixed intervals

• Long-term directional changes caused by economic growth or decline

• Unexpected disruptions in demand due to natural disasters

During the Great Depression, which began in 1929, what pattern would most likely be observed in a time series tracking economic activity over the subsequent years?

• A seasonal decline in demand occurring each winter due to changing weather conditions (seasonal variation)

• A temporary surge in consumer spending during holidays despite the downturn (irregular variation)

• A consistent upward trend driven by continuous technological growth (trend component)

• 1. A long-term decline followed by gradual recovery reflecting economic contraction and expansion (cyclical variation)

In which of the following situations would a business most appropriately rely on qualitative forecasting methods?

• A tech startup is launching a product in a new market with insufficient data, so it gathers expert opinions from industry analysts.

• A retailer uses three years of historical sales data to forecast monthly demand through moving averages.

• A manufacturer predicts future demand based on correlations between advertising spend and unit sales.

• A logistics company applies exponential smoothing to track shipment volume trends over time.

A beverage company wants to forecast demand for its bottled iced tea by analyzing external factors like average daily temperature and advertising spend. Which forecasting method would be most appropriate for this situation?

• Time series forecasting, since past sales trends alone provide enough insight without considering outside factors.

• Qualitative forecasting, because expert opinions will offer the most accurate projection in this data-driven scenario.

• Naïve forecasting, where the previous month's sales are assumed to continue without adjustment.

• Associative forecasting, because demand is expected to correlate strongly with weather conditions and marketing efforts.

The following statements describe the Naïve forecasting method, except:

• It incorporates current market conditions to adjust forecasts for greater accuracy.

• It assumes that the most recent actual value is the best predictor for the next period.

• It is often used as a baseline to compare the performance of more complex forecasting models.

• 1. It is best suited for data that is stable with minimal trend or seasonal influence.

A demand schedule for the last two years was given to management in May. It highlighted that the business experiences peak demand every 2nd quarter. Management failed to take advantage of this because… 

• The forecast was inaccurate, relying on outdated data and assumptions. 

• The forecast was unreliable, with inconsistent predictions year to year.

• The forecast, though accurate, was not timely and reached management too late to act. 

• The forecast was meaningful but focused only on qualitative trends.