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The correct choice, which is multiple linear regression, is used to project future demand by taking into account multiple variables simultaneously. This statistical method allows for the analysis of relationships between one dependent variable and two or more independent variables, enabling a more nuanced understanding of how various factors influence outcomes.

For instance, if a company wishes to forecast product demand, multiple linear regression might consider several predictors such as pricing, advertising expenditure, seasonality, and economic indicators. By incorporating these multiple variables, organizations can develop more accurate and comprehensive models that reflect the complexity of real-world scenarios.

In contrast, simple linear regression focuses on the relationship between only two variables, making it less suitable for scenarios where multiple predictors are at play. Qualitative forecasting relies on expert opinions or anecdotal evidence rather than statistical analysis, limiting its efficacy in data-driven decision-making. Time series analysis, while valuable for identifying trends over time based on historical data, does not inherently incorporate multiple independent variables in its basic form. Thus, multiple linear regression stands out as the appropriate method for projecting future demand when multiple factors are involved.