On this part we shall consider a curious example, I must say. As said our reference for this case study [7, p. 74]: "If we were the only ones in the world with access to this info, we could be the best Boston real-estate investors in 1978! Unless, somehow, someone were able to build an even more accurate estimate . . This is the Boston House problem. Essentially, the problem is used as benchmark for machine learning, generally, on competitions. "to estimate the median value of the house prices in a neighborhood (MEDV) given all the input features from the neighborhood." This problem is different from the previous one only because we have several inputs instead of just one. This problem is closer from reality since most problem, at least the one that can be useful, will have to do more than humans can do either with simple models or by head; and machine learning is good at it! As long as you have the computer power, and time to wait, they solve it with their feet on their backs, if they have any! One interesting reflection we shall do is regarding interpreting their inner workings, beyond just prediction. Prediction is the process by which we want to know what is next in time, on a system (e.g., stock market or demands on a company). "Is there any way to peek inside the model to see how it understands the data?….

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set.