A clothing company wants to predict sales figures based on the amount spent on advertising. Which type of regression analysis should this company use?

Multiple linear regression is utilized when predicting a dependent variable based on multiple independent variables. In this case, the company is only interested in the relationship between one independent variable (advertising spend) and one dependent variable (sales), making simple linear regression a more suitable choice.

Time series regression focuses on data collected over time to identify trends, cycles, or seasonal variations. This method would not be suitable here since the question does not involve predictions based on historical sales data over time but rather on the relationship between advertising spend and sales figures at a specific point.

Logistic regression is used for predicting binary outcomes, such as success/failure or yes/no decisions, rather than continuous values. Since the clothing company is looking to predict numerical sales figures, logistic regression is not applicable in this context.