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 should be used for predicting sales figures based on advertising spend.
Linear regression is suitable for predicting continuous outcomes, such as sales figures, based on one or more predictor variables, in this case, the amount spent on advertising.
A) Multiple linear regression
Multiple linear regression is used when there are multiple independent variables predicting a single dependent variable. While it could be applicable if several factors were considered, the question specifically indicates a focus on predicting sales based solely on advertising spend, making linear regression the more direct choice.
B) Time series regression
Time series regression is utilized for analyzing data points collected or recorded at specific time intervals. This type of regression is not relevant here, as the question does not involve temporal data but rather a relationship between advertising spending and sales figures.
C) Linear regression
Linear regression is the correct choice as it models the relationship between a dependent variable (sales) and an independent variable (advertising spend) using a linear approach. This method effectively allows the company to predict sales based on varying levels of advertising expenditure.
D) Logistic regression
Logistic regression is used for predicting binary outcomes, such as yes/no or success/failure scenarios. This method is inappropriate for the question's context, which involves predicting a continuous variable (sales figures), not a categorical one.
Conclusion
Linear regression is definitively the right choice for this scenario, as it directly addresses the relationship between advertising spend and sales figures. All other options do not fit the requirement of predicting a continuous variable based on a single predictor, rendering them unsuitable for the company's needs.