It's HW on Liniar and multiple regression models: It is 1970 and you are hired by a real estate company in Boston as a data consultant. The company would like to make price estimates based on different preferences that a customer can have, so that they can assess if a customer's budget is realistic or not. Your task is to build a model and write an executive summary that outlines important features that affect the price of a house. Attach "Boston" data included in the MASS library. The data contains information from 504 geographic areas. There are 14 attributes in each area of the dataset. You may find the description of each attribute using help(Boston) code in R. Your model should have the log transformation median house value (log(medv)) as the output variable. When you are searching for a meaningful regression equation, consider the following attributes: 1. Structural properties of a house (age, number of rooms, lot size allowed by zoning laws in the area) 2. Accessibility (distance to major employment centers and closeness to highways) 3. Neighborhood (crime rate, education quality, whether it is by the Charles River or not) You may include other terms in your model, but you must discuss a model that contains the features above. You can use model diagnostics or other model selection methods to guide you in determining a final model. Decide on one model and use it for the technical summary. Please provide Technical Summary? (Provide the R code and outputs) In the technical summary you speak to your peers. You show them that you performed a reasonable analysis and that you interpreted the results competently. This should not be a step-by- step report of what you did in R, but a summary of the most important steps in logical, not chronological, order. Even in a technical summary it is not of interest to hear, for example, how you used R; it is simply assumed that you know how to execute the available software. The technical narrative should explain what values were used in the executive summary and how they were rounded. In addition, it should explain what contributions to the model were neglected because their effect on house value is too small. The technical summary should, among other things, explain the final fitted model, term by term and estimate by estimate. It should mention model diagnostics that were performed and their outcomes, possibly accompanied by plots. Report data points (geographical areas) that you may have removed, if any, and why you did so: