代做UCI Machine留学生、代写R编程设计、代写DATA ANALYTICS、R语言程序代做

日期：2018-10-28 09:50

SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTING

BIG DATA AND DATA ANALYTICS

LAB PROJECT 4

This lab project is based on a housing dataset of suburbs in Boston. The dataset is available from the

UCI Machine Learning Repository (Lichman, 2013):

http://archive.ics.uci.edu/ml/datasets/Housing

EXERCISE 1 (2 MARKS) [R-CODE]

Use R to perform a multiple linear regression that regresses MEDV on CRIM (per capita crime rate by

town), RM (average number of rooms per dwelling), NOX (nitric oxides concentration; parts per 10

million), DIS (weighted distances to five Boston employment centres), and AGE (proportion of owneroccupied

units built prior to 1940). Interpret the coefficients and report the results of the regression

in APA style (including a regression table and reporting of F-values).

EXERCISE 2 (1 MARK) [R-CODE]

Use R to create a new factor variable called NOXCAT

that categorizes the suburbs into towns with LOW,

MEDIUM, and HIGH nitric oxides concentration (based

on the variable NOX). The categorization should be as

follows:

- LOW (<= 30% Quantile)

- MEDIUM (> 30% Quantile & <= 70% Quantile)

- HIGH (> 70% Quantile)

Then, use ggplot to create a boxplot that shows MEDV

for the different values of NOXCAT (LOW, MEDIUM,

HIGH).

EXERCISE 3 (2 MARKS) [R-CODE]

The newly created variable NOXCAT is a categorical variable with three possible values (LOW,

MEDIUM, and HIGH). Use R to manually create a set of dummy variables (for different values of

NOXCAT) and then regress MEDV on the different NOX categories. The coding of the dummy

variables in the regression should be such that the intercept reflects the MEDV value of suburbs in

the MEDIUM category. Interpret the coefficients.

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EXERCISE 4 (1 MARKS) [R-CODE]

Use ggplot() to create a scatterplot of MEDV by

LSTAT. Add a linear fit (red), a quadratic fit

(green), and a cubic fit (blue) to the plot.

EXERCISE 5 (2 MARKS) [R-CODE]

Use Leave-One-Out Cross-Validation (LOOCV) to compare a linear model, a quadratic model, a cubic

model, and a quartic model to regress MEDV on LSTAT. Interpret the results based on the meansquared

error (MSE).

EXERCISE 6 (2 MARKS) [R-CODE]

Use 11-fold cross-validation to compare 8

different degrees of polynomials to regress

MEDV on LSTAT. Use ggplot() to plot the mean

squared error (MSE) over the 8 different

degrees of polynomials. Interpret the results

based on the MSE. Why is 11-fold crossvalidation

in this particular case advantageous

compared to 10-fold cross-validation?

REFERENCES

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA:

University of California, School of Information and Computer Science.

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DATASET

Housing Housing Dataset

Description

A dataset about housing values in suburbs of Boston at the end of the 1970s.

Usage

Housing

Format

A data frame with 506 observations on the following 14 variables.

ID Town identifier

CRIM per capita crime rate by town

ZN proportion of residential land zoned for lots over 25,000 sq.ft.

INDUS proportion of non-retail business acres per town

CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

NOX nitric oxides concentration (parts per 10 million)

RM average number of rooms per dwelling

AGE proportion of owner-occupied units built prior to 1940

DIS weighted distances to five Boston employment centres

RAD index of accessibility to radial highways

TAX full-value property-tax rate per $10,000

PTRATIO pupil-teacher ratio by town

LSTAT % lower status of the population

MEDV Median value of owner-occupied homes in $1000's

Source

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine,

CA: University of California, School of Information and Computer Science.

Harrison, D., & Rubinfeld, D.L. (1978). Hedonic prices and the demand for clean air. Journal of

Environmental Economics & Management, 5, 81-102.