Forecasting Techniques

Correct Answer: It minimizes group bias through anonymous feedback rounds

The Delphi method's primary advantage is its ability to minimize group bias through anonymous feedback rounds. This is significant because: - It prevents dominant personalities from influencing others' opinions - Participants can express their views honestly in anonymity - Multiple rounds of feedback allow for refined consensus building - Experts can revise their opinions based on anonymous collective input The other options are incorrect because: - Face-to-face discussions can actually introduce bias and pressure to conform - The Delphi method is not primarily about statistical modeling or precise numerical predictions - Group majority voting and public debates would defeat the purpose of anonymous feedback and could reintroduce the group dynamics the method aims to avoid The structured, iterative nature of anonymous feedback is what makes the Delphi method particularly effective for gathering expert opinions while reducing social pressures and group dynamics that might skew results.

Correct Answer: Multiple regression analysis

Multiple regression analysis is the most appropriate forecasting technique for project scheduling when dealing with multiple variables and their correlations. Here's why: Multiple regression analysis: - Analyzes relationships between multiple independent variables and a dependent variable - Accounts for complex interactions between various project factors - Provides statistical validity and reliability in predictions - Commonly used in project management for schedule forecasting Why other options are less suitable: Exponential smoothing is primarily used for time series data with seasonal patterns, but doesn't effectively handle multiple variable correlations. Monte Carlo simulation focuses on risk analysis through random sampling, but isn't specifically designed for analyzing variable correlations. Moving average analysis is too simplistic for complex variable relationships, as it mainly focuses on historical trends of single variables.

Correct Answer: Exponential smoothing analysis

Exponential smoothing analysis is the correct answer for project scheduling forecasting because: Exponential smoothing analysis is specifically designed to give more weight to recent data points while gradually reducing the influence of older historical data. This makes it particularly useful in project scheduling where recent trends are often more relevant than older data. The other options are incorrect because: Rolling average trend calculation with seasonal adjustment factors gives equal weight to all data points within the rolling window and primarily focuses on seasonal patterns, which isn't the main focus of the question. Bayesian probability distribution modeling is a complex statistical method that updates probabilities based on new evidence, but doesn't specifically focus on weighted averages of time-series data. Neural network time series prediction uses complex algorithms and machine learning but doesn't necessarily apply weighted averages in the manner described in the question.