Chapter 6 homework
I completed these homework problems before writing the examine.mod.multiple() function so you will
not see it implemented here.
6.15
a. No unusual observations
b.
1.8 2.0 2.2 2.4 2.
Chapter 10 homework
Below are partial answers to the problems.
10.7
a.
> #Read in the data
> patient<-read.table(file = "C:\chris\UNL\STAT870\Instructor_CD\Data
Sets\Chapter 6 Data Sets\CH06PR15.txt",
Homework 2 Solution
1. (Sec. 3.4 Problem 4). (20 points)
Model (3.8):
(a) No. Here are the weakness. 1) The distributions of response and predictor variables are highly skewed.
2) The variance of rand
Homework 3 Solution
1. (a) It means there is not much evidence that Y and X1 are linearly correlated given X2 being held
constant.
(b) No. We should consider
Y = 0 + 1 X1 + ,
and consider the test
H0
Full name:
UNL ID:
Anh P. Le
24064626
HOMEWORK 1 STAT870
1. 2.8.1
n = 18
t/2, n-2 = t0.25, 16 = 1.98
Scatter plot for linear regression model of the gross box office results for the current week vs.
p
From p. 23-4 of KNN, formal proofs of 1-6.
1.
ei = ( Yi Yi )
n
n
i=1
i=1
n
= ( Yi b0 b1Xi )
i =1
()
= ( Y ) n ( Y b X ) nb X
= ( Y ) nY
n
n
i=1
i=1
= Yi nb0 b1 Xi
n
i=1
i
1
1
n
i=1
i
=0
n
n
2
2
2
2
=
Chapter 11 homework
Below are partial answers to the problems.
Reproduce the example on p. 427 of KNN. Below is my R code and output.
> set1<-read.table(file = "C:\chris\UNL\STAT870\Instructor_CD\Data
Chapter 9 homework
Below are partial answers to the problems.
9.10
Answer key:
Additional results
Be careful with reading in the data. Y is the first column, not X 1.
a. Possible unusually small value
Chapter 8 homework
Below are partial answers to the problems.
8.38
Answer key
Additional results
Without doing the mean adjustment to X:
> lm(formula = nurses ~ facilities + I(facilities^2), data = se
Chapter 7 homework
Below are partial answers to the problems.
7.5
a.
>
mod.fit<-lm(formula = satisfaction ~ illness + age + anxiety, data = patient)
>
anova(mod.fit)
Analysis of Variance Table
Respons
Chapter 5 homework
5.4
y1
y n
2
Y = [ y1,y2 ,.,yn ] = yi2 = 7.82 + 9.02 + 10.22 + 112 + 11.72 = 503.77
Y
M i=1
yn
1 x12
n
1 x n
xi2
1 L 1
5 0
1
22
i =1
=
X X =
=
n
n
2
x12 x22 L xn2 M M x
Chapter 4 homework
4.3 Here is my code and the corresponding output.
b.
> #
> # 4.3b
>
>
>
mod.fit<-lm(formula = minutes ~ copiers, data = copier)
sum.fit<-summary(mod.fit)
sum.fit
Call:
lm(formula =
Chapter 2 homework
2.2 Do on your own.
2.5
a) Here is my code and the corresponding output. Note that I edited this code and output after
pasting it into Word to make it look nicer. Again, you are exp
Chapter 1 homework
1.20
a) Here is my code and the corresponding output. Note that I edited this code and output after
pasting it into Word to make it look nicer. Again, you are expected to do this as