Statistics 108
Homework 1 (Due Friday, Oct 7)
Problems: 1.6, 1.7, 1.22, 1.26, 1.29, 1.30, 1.33
1.6 (b) 0 is the value of Y when X=0. 1 is the slope of the regression line. It is the corresponding change i
Homework 1 Solutions
#1.5
No. The simple linear regression model is
#1.6
For : The expected value of Y when X=0 is 200.
For : When X increases by one unit, the expected value for Y increases by 5.0.
#1.20
a)
>data=read.table("CH01PR20.txt",header=T)
>atta
STA108 Homework7
due: 03/04/2015, Wed, in class
1. Simple linear regression in matrix form. In this problem, you will perform linear
regression of a response Y on a predictor X using matrix algebra (Your calculations should
be solely based on matrix algeb
Homework 2 Solutions
#2.1 a
Since the 95% confidence interval for 1 does not include 0, then we would reject 0 : 1 = 0 at the 0.05
significance level. So the students conclusion is warranted.
#2.4
a)
>
>
>
>
>
>
>
data = read.table("CH02PR04.txt", header
R Output Problems
Spring 2015
STA 108
Copier Maintenance
The Tri-City O ce Equipment Corporation sells an imported copier on a franchise
basis and performs preventative maintenance and repair service on this copier. The
data below have been collected from
Statistics 108
Homework 5
Due : November 10, 2014 (Monday), In Class
* Put your name and your section number on your homework.
1. Simple linear regression in matrix form. In this problem, you will perform linear
regression of a response Y on a predictor X
STA108 Homework2
due: 01/21/2015, Wed, in class
1. Problem 1.7
Solution:
(a) No, because we dont know any distribution assumptions of error terms here.
Hence we dont have any distribution of Yi as well. Therefore we couldnt estimate
the probability for Y
Homework 2 Solutions
#2.1 a
Since the 95% confidence interval for 1 does not include 0, then we would reject 0 : 1 = 0 at the 0.05
significance level. So the students conclusion is warranted.
#2.4
a)
>
>
>
>
>
>
>
data = read.table("CH02PR04.txt", header
Homework 3
Spring 2015
STA 108
Problem 14
15
20
25
30
35
a) The boxplot looks pretty symmetric with no obvious outliers
c) The residual vs. tted value plot can help to diagnose heteroscedasticity (nonconstant variance), as well as any patterns in the resi
STA108 Homework4
due: 02/04/2015, Wed, in class
1. Problem 2.10
Solution:
(a) prediction interval (b) condence interval (c) prediction interval
2. Problem 2.17
Solution:
> 0.33 and conclude H0
An analyst concluded that Ha : = 0 which means the null hypot
STA108 Homework3
due: 01/29/2015, Friday, in class
For all questions you must show your work. This enables us to understand your
thought process, give partial credit and prevent crude cheating.
If you use R then you must turn in a printout of your R outpu
STA108 Homework2
due: 01/22/2015, Friday, in class
For all questions you must show your work. This enables us to understand your
thought process, give partial credit and prevent crude cheating.
If you use R then you must turn in a printout of your R outpu
Statistics 108
Homework 5
Due : November 10, 2014 (Monday), In Class
* Put your name and your section number on your homework.
1. Simple linear regression in matrix form. In this problem, you will perform linear
regression of a response Y on a predictor X
STA 108: Applied Statistical Methods: Regression Analysis
Practice Final Exam Solution
December 19, 2014: 8:00-10:00am
Print name:
Print section number:
Print student ID (last four digits):
Sign name:
1
Instructions: This is a closed book exam. Two pages
STA 108
Regression Analysis
Fall 2015
Homework 2 Solution
Assignment:
Textbook problems 2.7, 2.17, 2.18, 2.19, 2.26, 3.6 (only a-c), 3.9, 3.17
(in 3.17b you may calculate R2 instead of SSE and choose the transformation that maximizes R2 )
The R code used
Homework 5 Solutions
ACM/ESE 118, Fall 2008
Date: 11/25/2008.
(1) Air Pollution and Mortality
Before going on to a more detailed analysis, as a rst pass we can try simply tting all
the data using all the regressors. We get the following:
Coefficients:
Est
Stat 108 HW #5 Solution
5.22 Y12+ 3Y22+ 9Y32+8 Y1Y3
6.15 a. One way of doing the stem-
and-
leaf plots (there are other ways of choosing
the stems, which may be also ac
15
5
10
Dataset$Y
20
25
Homework 7 Solution
a. Y=21.09+1.14C-
0.12C2, here C = X-
mean(X)
10
15
20
25
Dataset$X
The solid black dots represent the fitted regression function. The quadratic regression
function does a
STA 108 B1-B2 Spring 2015
MIDTERM 1
Note: This is a closed book, closed notes exam. You can bring one page {twosided) with your own
handwritten notes and a handheld calculator. For all problems, give brief but complete solutions. You
must show your work
Statistics 108
Homework 8
Not Due
1. Diabetes data. This data consist of 16 variables on 403 subjects from 1046 subjects who
were interviewed in a study to understand the prevalence of obesity, diabetes, and other
cardiovascular risk factors in central Vi
1
Homework 3
Due in class, Feb. 8th
STA 108, Winter 2013
Problem 1 (Airfreight Breakage)
Refer to the Airfreight Breakage data in homework 1
i:
Xi :
Yi :
1
1
16
2
0
9
3
2
17
4
0
12
5
3
22
6
1
13
7
0
8
8
1
15
9
2
19
10
0
11
a. Set up the ANOVA table. (calc
Statistics 108
Homework 2
Due : October 17, 2014, In Class
* Put your name and your section number on your homework.
1. For the simple linear regression model (1.1) in the textbook, show the following properties of
the residuals ei = Yi Yi .
(a)
(b)
n
i=1
Solutions to Project 1, STA 108
Part I:
Part a): Let Y = the number of active physicians, X1 = total population, X2 = number of hospital beds,
and X3 =total personal income. Since we are interested in seeing how the number of active physicians varies,
our
1
Homework 4
Due in class, Feb. 15th
STA 108, Winter 2013
Problem 1 (Grade Point Average)
Refer to Grade point average data in Problem 1.19. The data can be downloaded or read into R from:
http:/www.stat.lsu.edu/exstweb/statlab/datasets/KNNLData/CH03PR03.
Statistics 108
Homework 6
Due : November 26, 2014 (Wednesday), In Class
* Put your name and your section number on your homework.
1. A multiple linear regression case study by R (contd). You may quote results from
homework 5.5. Please also attach your R c
Statistics 108
Homework 1 Solution
1. When asked to state the simple linear regression model, a student wrote it as follows: Ecfw_Yi =
0 + 1 Xi + i . Do you agree? Why?
No. The simple linear regression model is Yi = 0 + 1 Xi + i . When we take the expect
1
Homework 8
Due in class, March 15th
STA 108, Winter 2013
Problem 1
State the number of degrees of freedom that are associated with each of the following extra sums of
squares:
1. SSR(X1 |X2 )
df [SSR(X1 |X2 )] = 1
2. SSR(X2 |X1 , X3 )
df [SSR(X2 |X1 , X
Statistics 108
Homework 2 Solution
Due : October 17, 2014, In Class
* Put your name and your section number on your homework.
1. For the simple linear regression model (1.1) in the textbook, show the following properties of the
residuals ei = Yi Yi .
(a)
1
STA108 summer 2016 Final solutions
11/29/16 I am posting this now without all of the solutions finished because I know some of you want to
begin preparing for the final now. I am still working on writing the solutions for this exam. I will repost on
Sma
1
STA108B fall 2016 Homework 8 solutions
PART A: No interaction model
1. Plot the data. You dont need to write anything for this question. Just look at the
data and think about what you see. As usual, wait until you have added the regression
lines before
1
STA108 fall 2016 Homework 4
Due at the beginning of class on Monday October 24, 2016
A nice short homework assignment since you have a midterm this week.
No data or R programming for this assignment
Write your section number on the right side at the top
1
STA108B fall 2016 Homework 10
12/1 10:30am two small typos fixed. One in the R code for question 7, which had the
wrong denominator (the sample size, which is now n2, but was mistake said n1). If you
have already used that code to compute the percents (
1
STA108 fall 2016 Homework 7 solutions
Question 1 is a model with interaction between a categorical variable and a continuous
variable.
Question 2 is a model with an interaction between two categorical variables.
Question 3 is a model with an interaction