Consider the Boston housing dataset. This small data set provides information about houses in different areas in Boston. The following table provides the dictionary of the variables in the data set.

Variables	Variable description
CRIM	Crime rate
ZN	Percentage of residential land zoned for lots over 25,000 ft2
INDUS	Percentage of land occupied by nonretail business
CHAS	Does tract bound Charles River (= 1 if tract bounds river, = 0 otherwise)
NOX	Nitric oxide concentration (parts per 10 million)
RM	Average number of rooms per dwelling
AGE	Percentage of owner-occupied units built prior to 1940
TAX	Full-value property tax rate per $10,000
PTRATIO	Pupil-to-teacher ratio by town
LSTAT	Percentage of lower status of the population
MEDV	Median value of owner-occupied homes in $1000s
CAT.MEDV	Is median value of owner-occupied homes in tract above $30,000 (CAT.MEDV = 1) or not (CAT.MEDV = 0)
DIS	Weighted distances to five Boston employment centers
RAD	Index of accessibility to radial highways

We have 2 variables as target variables used by Multiple Linear Regression and Decision Tree modeling. For the linear regression model, MEDV is the target variable and CAT.MEDV is the target variables for the decision tree model developed in this assignment. Students must not consider them as predictor variables. With respect to the Boston housing data set, answer the following questions using R software. You must provide answer, R codes and the results of analysis in the solution document.

Apply str() and summary() commands and display the findings. What is the range of Tax and Age.

CAT.MEDV is the median value of homes: 0 = value < $30000; 1 = value > $30000. Change this variable to a 2 level categorical (factor) variable and save in the dataset. Demonstrate R codes and their results.

Generate a training with 400 rows and a test set with 106 rows from the given dataset.

Develop a multiple regression model to predict MEDV. Consider all other variables as predictors (except CAT.MEDV). Provide a summary of results.

Which variables are statistically significant?

Evaluate the prediction performance of the model using R-square and RMSE parameters. Explain your findings.

Develop a decision tree model to predict CAT.MEDV. Consider all other variables as predictors (except MEDV). Provide a summary of results.

Generate a training with 400 rows and a test set with 106 rows from the given dataset. Create a confusion matrix for the test set. Explain the results in terms of overall prediction error.