Homework #6:
P.825-833 (13.13, 13.14, 13.16, 13.34, 13.35), and the additional problems below.
Due Friday, March 20 .
Some Notes on the Homework :
The data for problem 13.34 and additional problems 1
Comments - Homework #6
1. How categorical variables enter models: In problem 13.13, an industry type variable
was dened as a 0, 1, 2, or 3 depending on which of four industry types an observation
was,
Logistic Regression - An Overview (12.8)
Logistic regression models are regression models for binary response variables. To see when
such models might be applicable, consider the following example.
Ex
Homework #7:
P.828-864 (13.6(a,b only), 13.24, 13.63, 13.64), and the additional problems
below. Due Friday, March 27 .
Some Notes on the Homework :
The data for Problem 13.11 (aphid.txt) and additio
# Reads in the Pima Indian diabetes data
# =
library(faraway) # Loads faraway library
data(pima) # Loads pima data
# Scatterplot of diabetes incidence vs. glucose concentration
# =
plot(pima$glucose,p
Solutions - Homework #1
1. Problem 11.62
(a) The relationship between price and income is essentially linear, although the house
with the highest income deviates slightly from this linear pattern and
Solutions - Homework #6
1. Problem 13.13
(a) If the industry variable is dened simply as a 0, 1, 2, or 3 depending on the type of
industry, this imposes a linear ordering on the 4 industry categories
Homework #5:
P.738-754 (12.32, 12.49), & the 5 additional problems below. Due Monday, March 9 .
Some Notes on the Homework
The data for additional problems 3, 4, & 5 are available on the course webpa
Homework #1:
P.648-655 (11.62, 11.63 (modied - see below), 11.64, 11.77, 11.78), and the
additional problems below. Due Friday, February 6 .
Some Notes on the Homework :
The data for 11.62 and additi
Nonlinear Regression Models (13.3)
Recall that a parametric model is said to be linear if it is a linear function of the model
parameters. Any model which is linear can be written in the form of a gen
Looking for LOF with Residual Plots
In previous lectures, we have discussed in vague terms three main steps in regression model
development. These three steps were given as:
1. Variable Selection - id
Analysis of Variance (ANOVA) (8.1-8.2)
Much of statistical inference centers around the ability to distinguish between two or more
groups in terms of some underlying response variable y. For example,
# Performs Kruskal-Wallis test of failure time data
# =
time <- c(105,3,90,217,22,76,43,1,37,14, # Vector of failure times
183,144,219,76,39)
location <- as.factor(rep(1:3,each=5) # Vector of locatio
The Factorial Design (14.3-14.5)
This handout introduces the Factorial Design, the purpose of such a design, model form,
analysis, and interpretations which can be made. To this point, we have studied
Kruskal-Wallis Test (8.6)
As mentioned in the homework, the Kruskal-Wallis test is a nonparametric alternative to
the 1-way ANOVA procedure for comparing distributions for multiple populations.
It is
nic <- read.csv("Data/nicotine.txt",header=T) # Reads in nicotine data
par(mfrow=c(1,2) # Creates 1x2 graphics window
boxplot(leafsize~condition,xlab="Condition",data= # Boxplot for leaf sizes by
n
Examining the ANOVA Variance Homogeneity Assumption (7.4, 8.4)
In ANOVA problems, variance homogeneity refers to having equal population variances for
each of the t populations or treatments considere
# Scatterplot & Residual plot for Extinction data
# =
extinct <- read.csv("Data/extinct.txt", # Reads in the extinction
header=T) # data
logtime <- log(extinct$exttime) # Vector of log extinction tim
Model Diagnostics
The term model diagnosticsrefers to methodology for examining whether or not there are
problems in a model. We will discuss problems of two basic types: those involving the model
ass
Inference in Multiple Regression w/Example (12.3, 12.4)
The purpose of this handout is to consider modeling some data with a multiple linear regression model and to discuss some of the types of infere