Question 18

Linear regression is the appropriate method for predicting sales figures based on advertising expenditure.

Linear regression enables the clothing company to model the relationship between the independent variable (amount spent on advertising) and the dependent variable (sales figures), allowing for simple and effective predictions.

A) Linear regression

This option is correct because linear regression is specifically designed to analyze the relationship between a single independent variable and a dependent variable. In this case, it allows the company to directly measure how changes in advertising spending are associated with changes in sales figures.

B) Time series regression

Time series regression is not suitable here as it focuses on analyzing data points collected or recorded at specific time intervals to identify trends over time. Since the question pertains to predicting sales based solely on advertising spending rather than over a period, this option does not apply.

C) Multiple linear regression

While multiple linear regression could be used if there were several independent variables influencing sales, the question specifies a single variable: advertising expenditure. Therefore, using multiple linear regression would be unnecessarily complex for this scenario.

D) Logistic regression

Logistic regression is used for predicting binary outcomes, such as yes/no or success/failure scenarios. Since the company is interested in predicting sales figures, which are continuous rather than categorical, this option is incorrect.

Conclusion

Linear regression is definitively the right choice for the clothing company as it directly addresses the need to predict sales figures based on advertising expenditure. Other options fail to meet the criteria set by the question, either by addressing multiple variables, focusing on time-related data, or dealing with categorical outcomes.