代做UCI Machine留学生作业、代写R编程设计作业、代写DATA ANALYTICS作业、R语言程序作业代做
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.
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