Explore how linear regression is used to understand the relationship between housing prices and variables like square footage, bedrooms, and neighborhood. Learn why it’s the most suitable method for real estate analysis.
Table of Contents
Question
In a bustling city, Emma, a real estate agent, is tasked with predicting housing prices for prospective buyers. She collects data on various factors such as square footage, number of bedrooms, neighborhood, and distance to amenities. Emma wants to understand how changes in these variables impact the selling price of homes.
Which of the following methods would Emma most likely use to analyze the relationship between housing prices and factors such as square footage, number of bedrooms, and neighborhood?
A. Logistic algorithm
B. Analytical regression
C. Decision tree
D. Linear regression
Answer
D. Linear regression
Explanation
Linear regression allows Emma to input the variables and calculate a reasonably good prediction of how those variables will affect housing prices.
Emma would most likely use linear regression to analyze the relationship between housing prices and factors such as square footage, number of bedrooms, and neighborhood.
Linear regression is a statistical method used to model the linear relationship between a dependent variable (in this case, housing prices) and one or more independent variables (square footage, bedrooms, neighborhood, etc.). It helps determine how changes in the independent variables impact the dependent variable.
Here’s why linear regression is the most suitable method for Emma’s analysis:
- Continuous dependent variable: Housing prices are typically continuous numerical values, which is a requirement for linear regression.
- Multiple independent variables: Linear regression allows for the inclusion of multiple independent variables, such as square footage, bedrooms, and neighborhood, to understand their individual and combined effects on housing prices.
- Interpretability: The coefficients obtained from linear regression provide clear interpretations of the relationship between each independent variable and the dependent variable. For example, the coefficient for square footage would indicate how much the housing price changes with a one-unit increase in square footage, holding other variables constant.
- Prediction: Once the linear regression model is built, it can be used to predict housing prices for new data points based on the values of the independent variables.
The other options are less suitable for Emma’s analysis:
- Logistic regression is used for binary classification problems, not continuous dependent variables like housing prices.
- Decision trees are used for classification and regression tasks but do not provide the same level of interpretability as linear regression for understanding the relationship between variables.
- Analytical regression is not a commonly used term in this context.
Therefore, linear regression is the most appropriate method for Emma to analyze the relationship between housing prices and the given factors.
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