Result of regression of house prices on house characteristics using King County in USA

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Topic: Result of regression of house prices on house characteristics using King County in USA

Elaboration: The subject matter is accessible for academic research and holds real-world relevance. It is well-suited for the application of multiple linear regression analysis. The variable that we aim to predict, known as the Dependent Variable, is the price of the house. The factors that could influence this price, referred to as Independent Variables, encompass several aspects such as: Size of the house, Number of bedrooms, Number of bathrooms, Location (e.g. city or neighbourhood), Age of the house, Proximity to amenities (e.g., schools, parks, public transport)

Model Building:

Using the collected data, a multiple linear regression model is built. The general form of the equation for this model could be:

Price=0+1×Size+2×Bedrooms+3×Bathrooms+4×Location+5×Age+6×Proximity+

Where:

- β0 is the intercept

-1to 6 are the coefficients for each independent variable

- ϵ is the error term.