True or False: Multiple linear regression should be used if predicting house prices based on size, number of bedrooms, and garage availability.

True or False: Multiple linear regression should be used if predicting house prices based on size, number of bedrooms, and garage availability.

• A. True
• B. False
• C. Only for size and number of bedrooms
• D. Only for numerical variables

Answer: (A)

The statement is true because multiple linear regression is an appropriate statistical technique for predicting an outcome based on several independent variables. In the context of predicting house prices, size, number of bedrooms, and garage availability represent multiple different predictors of the dependent variable, which is the house price. In multiple linear regression, each predictor variable can be either numerical (like size) or categorical (like garage availability). The methodology allows for the inclusion of both types of variables, making it suitable for this scenario. By incorporating size and number of bedrooms along with garage availability into the regression model, it's possible to estimate how each of these factors contributes to the overall price of a house, allowing for a comprehensive model that reflects the influence of multiple factors simultaneously. Other provided options may limit the application of multiple linear regression incorrectly. For instance, stating that it should only be applied to certain types of predictors or excluding categorical variables does not align with how multiple linear regression is designed to function. This versatility is what enables it to be a powerful tool in predictive analytics, particularly in fields like real estate where multiple features influence pricing.

The statement is true because multiple linear regression is an appropriate statistical technique for predicting an outcome based on several independent variables. In the context of predicting house prices, size, number of bedrooms, and garage availability represent multiple different predictors of the dependent variable, which is the house price.

In multiple linear regression, each predictor variable can be either numerical (like size) or categorical (like garage availability). The methodology allows for the inclusion of both types of variables, making it suitable for this scenario. By incorporating size and number of bedrooms along with garage availability into the regression model, it's possible to estimate how each of these factors contributes to the overall price of a house, allowing for a comprehensive model that reflects the influence of multiple factors simultaneously.

Other provided options may limit the application of multiple linear regression incorrectly. For instance, stating that it should only be applied to certain types of predictors or excluding categorical variables does not align with how multiple linear regression is designed to function. This versatility is what enables it to be a powerful tool in predictive analytics, particularly in fields like real estate where multiple features influence pricing.