Which of the following is a requirement for using multiple linear regression?

Which of the following is a requirement for using multiple linear regression?

• A. Dependent variable should be binary
• B. Independent variables must be categorical only
• C. Independent variables can be both numerical and categorical
• D. Dependent variable must be categorical

Answer: (C)

In multiple linear regression, the dependent variable must be a continuous numeric outcome, rather than categorical or binary. However, the independent variables can take on various forms, which is why the correct answer emphasizes that they can be both numerical and categorical. This flexibility allows researchers to incorporate different types of predictors into their models, enriching the analysis and enabling the exploration of complex relationships within the data. For instance, a researcher may want to evaluate how continuous variables like temperature or sales figures interact with categorical variables like brand names or product types. Therefore, utilizing both types of independent variables allows for a more comprehensive and effective model when predicting the continuous dependent outcome. Understanding this framework is essential for correctly formulating and interpreting multiple linear regression analyses in business contexts.

In multiple linear regression, the dependent variable must be a continuous numeric outcome, rather than categorical or binary. However, the independent variables can take on various forms, which is why the correct answer emphasizes that they can be both numerical and categorical.

This flexibility allows researchers to incorporate different types of predictors into their models, enriching the analysis and enabling the exploration of complex relationships within the data. For instance, a researcher may want to evaluate how continuous variables like temperature or sales figures interact with categorical variables like brand names or product types. Therefore, utilizing both types of independent variables allows for a more comprehensive and effective model when predicting the continuous dependent outcome.

Understanding this framework is essential for correctly formulating and interpreting multiple linear regression analyses in business contexts.