A researcher wishes to predict the selling price of houses of City ABC using mul

A researcher wishes to predict the selling price of houses of City ABC using multiple linear
regression model. A random sample of 543 houses located in the non-core area that is sold in last
year were randomly selected to form a dataset “Housing.csv”.
The dataset includes the following eight variables:
Variable Description
price Price of the houses in $
area Area of a house in square feet
bedrooms Number of house bedrooms
bathrooms Number of bathrooms
stories Number of house stories
mainroad Whether connected to mainroad (Yes/No)
basement Whether has a basement (Yes/No)
parking Number of house parking
The dependent variable is “house_ price”. The “real_estate_valuation.csv” dataset can be downloaded
(a) Utilize R to determine the multiple linear regression model to predict the Price of houses by
considering which independent variable(s) be included in the model among the other given
variables using stepwise regression (forward). You are expected to perform relevant model
checking including relevant graphs plotting after the desired model is formulated. All R
programs must be included in the answer and marks will be deducted if failing to do so.
(40 marks)
(b) Perform relevant hypothesis testing to assess the validity of the multiple linear regression model
obtained as well as the validity of individual regression coefficients. (5 marks)
(c) Interpret the regression coefficients of the model. (5 marks)
(d) Write a reflective journal of not more than 200 words that summarizes your learning experience
in applying knowledge and skills acquired in the course to build the regression model for the
given problem, and that explain how this experience could enrich your ability to apply course
knowledge to real life applications. (10 marks)