Linear Regression models are the simplest linear models available in statistical literature. While the assumptions of linearity and normality seem to restrict the practical use of this model, it is surprisingly successful at capturing basic relationships and predicting in most scenarios. The idea behind the model is to fit a line that mimics the relationship between target variables and a combination of predictors (called independent variables). Multiple regression refers to only one target variable and multiple predictors. These models are popular not only for solving the prediction task but also for working as a model selection tools allowing to find the most important predictors and eliminate redundant variables from the analysis.
Everyone who has ever owned or lived in a house knows at least a little bit about the whims of the real estate market. Big houses cost more, neighborhood matters, proximity to basic services is great, age and style are important in some markets, you name it. But what is it that matters the most? This is a question that visualization can help us answer.