Forecasting methods knowledge check: Which of the following is a widely used statistical technique for forecasting future values from historical data?

Introduction / Context:
Forecasting translates historical data into predictions about future demand, prices, workloads, or other metrics. Among many techniques, some are specifically designed to model the relationship between predictors and an outcome variable over time or across observations.

Given Data / Assumptions:

• We seek a technique that estimates future values using explanatory variables or time as a predictor.
• Correlation measures association but does not produce a predictive equation.
• Linear programming is for optimization, not forecasting.
• Monte Carlo simulates uncertainty but is not, by itself, a forecasting regression.

Concept / Approach:
Regression analysis fits an equation relating an outcome (dependent variable) to one or more predictors (independent variables), supporting extrapolation and scenario analysis. It can be extended to time series (for example, AR, ARIMA with regression effects) and causal forecasting models.

Step-by-Step Solution:
Identify techniques in the list and their primary purpose.Select the one that builds predictive equations: regression analysis.Reject techniques that measure association (correlation), optimize (linear programming), or only simulate (Monte Carlo) without a fitted predictive relationship.

Verification / Alternative check:
Business analytics curricula list regression as a foundational forecasting method alongside exponential smoothing and ARIMA.

Why Other Options Are Wrong:

• Correlation analysis: Descriptive statistic; does not predict by itself.
• Linear programming: Allocation/optimization given constraints.
• Monte Carlo: Sampling-based risk analysis; may use regression inputs but is not inherently a forecasting model.
• None of the above: Incorrect because regression analysis is correct.

Common Pitfalls:
Confusing correlation with causation; relying on regression without checking assumptions and out-of-sample performance.

Final Answer:
regression analysis