62. A clothing company wants to predict sales figures based on the amount spent on advertising. Which type of regression analysis should this company use?
Answer: C
Linear regression is the appropriate method for predicting sales figures based on advertising expenditure.
Linear regression is ideal for modeling the relationship between a dependent variable, such as sales figures, and an independent variable, such as advertising spend. This method provides a straightforward means to quantify how changes in advertising budget can affect sales.
A) Multiple linear regression
Multiple linear regression is used when there are two or more independent variables predicting a single dependent variable. In this scenario, the company is only interested in the relationship between one independent variable (advertising spend) and one dependent variable (sales figures), making this option more complex than necessary.
B) Time series regression
Time series regression is utilized for data that is collected over time to identify trends, seasonal patterns, and cyclical behaviors. Since the question focuses on predicting sales based on a single factor rather than analyzing trends over time, this option is not applicable.
C) Linear regression
Linear regression is the correct choice for this scenario as it directly addresses the need to predict sales figures from a single independent variable, advertising spend. It allows for a clear understanding of the linear relationship and enables straightforward forecasting based on that relationship.
D) Logistic regression
Logistic regression is designed for modeling binary outcomes, where the dependent variable is categorical (e.g., yes/no). Since the scenario involves predicting continuous sales figures, this option is unsuitable and does not fit the context of the problem.
Conclusion
Linear regression is the most suitable method for this clothing company to predict sales based on advertising expenditures, as it effectively captures the direct relationship between the two variables. The other options either introduce unnecessary complexity or are entirely inappropriate for the type of data being analyzed, reinforcing that linear regression is the optimal choice for this prediction task.