Lecture 11
Fri, Feb. 8
(exam 1 covers through this lecture)
36-202
Spring 2013
(Finishing) Categorical Variables in Regression
[Reserve Material Reference:
Section 13.5, How Can Regression Include Categorical Predictors, of Statistics by Agresti and Frank
36-202 Homework 2
Spring 2010
This homework is due Wednesday, February 3, in class BEFORE class starts . HW submitted
after the assignments have been collected and before 3:00 PM will be penalized 20 points. No HW
will be accepted after 3:00 PM.
Please t
36-202 Homework 3
Spring 2010
This homework is due Wednesday, February 10, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your document in the rele
36-202 Homework 4
Spring 2010
This homework is due Wednesday, February 24, in class BEFORE class starts . No HW will
be accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your document in the rele
36-202 Homework 5
Spring 2010
This homework is due Wednesday, March 24, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your document in the relevan
36-202 Homework 6
Spring 2010
This homework is due Wednesday, March 31, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your document in the relevan
Problem 1
I.
Source
Drug
Error
Total
DF
2
21
23
S = 2.409
SS
10.27
121.91
132.19
MS
5.14
5.81
R-Sq = 7.77%
F
0.88
P
0.428
R-Sq(adj) = 0.00%
II.
Source
Drug
Subject
Error
Total
DF
2
7
14
23
S = 0.3674
SS
10.270
120.025
1.890
132.185
MS
5.1350
17.1464
0.135
36-202 Homework 7
Spring 2010
This homework is due Wednesday, April 7, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions.
Please staple your HW.
Do not forget to write your sectio
36-202 Homework 8 - Logistic Regression
Spring 2010
This homework is due Wednesday, April 21, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your d
36-202 Homework 9 - Logistic Regression
Spring 2010
This homework is due Wednesday, April 28, in class BEFORE class starts . No HW will be
accepted once the assignments have been collected.
Please type your solutions, and paste Minitab output into your d
Problem 1
(The solid circles represent the averages)
One-way ANOVA: Libido versus Dose
Source
Dose
Error
Total
DF
2
27
29
S = 1.833
Level
High
Low
Placebo
SS
20.72
90.75
111.47
MS
10.36
3.36
F
P
0.062
R-Sq =
N
13
8
9
Mean
4.846
5.250
3.222
StDev
2.115
1.2
PROBLEM 1
Fitted Line Plot
14
12
murder rate
10
8
6
4
2
0
16
18
20
22
24
single parent
26
28
30
Regression Analysis: murder rate versus single parent
The regression equation is
murder rate = - 9.85 + 0.626 single parent
Predictor
Constant
single parent
S
PROBLEM 1
(The solid black circles represent the averages)
Descriptive Statistics: Adapt.Score
Variable
Adapt.Score
Group
Group1
Group2
Group3
N
16
16
16
N*
0
0
0
Variable
Adapt.Score
Group
Group1
Group2
Group3
Q3
16.750
21.00
18.000
Mean
14.500
18.31
15.
36-202 STATISTICAL METHODS - Second Midterm
You must show your work and explain your steps in order to get full credit.
You should always comment on the numerical results.
You may use one one-sided sheet of notes (8.5 by 11 inches) and a calculator. You m
36-202 STATISTICAL METHODS - First Midterm
February 25, 2008
You must show your work and explain your steps in order to get full credit.
You should always comment on the numerical results.
You may use one one-sided sheet of notes (8.5 by 11 inches) and a
36-202 Homework 1
Spring 2010
This homework is due Wednesday, January 27, in class BEFORE class starts . The assignments
will be taken from the classroom 10 minutes after class starts. See syllabus for late HW policy.
Please type your solutions, and past
36-202 Homework 9 solutions
PROBLEM 1: 24 points
1(a). 8 points
Holding all other variables xed, for every one year increase in the fathers education, the
odds that his child obtains a high school degree are multiplied by e2 = e0.13 1.14. In
other words,
36-202 Homework 8 solutions
PROBLEM 1: 5 points
p = .08, so the odds are
p
1p
=
.08
.92
=
8
92
=
2
23
The odds of a male being color blind are 2 to 23.
PROBLEM 2: 5 points
p
1p
= 11
7
7p = 11(1-p)
7p + 11p = 11
18p = 11
p = 11 = .6111
18
PROBLEM 3: 10 poi
Lecture 12
Mon, Feb. 11
(this lecture begins exam 2 material)
36-202
Spring 2013
One-Way ANOVA (Analysis of Variance)
[Reserve Material Reference:
Chapter 14 of Statistics by Agresti and Franklin;
Chapter 12 of Introduction to the Practice of Statistics b
Lecture 14
Fri, Feb. 15
36-202
Spring 2013
Beginning Two-Way ANOVA
[Reserve Material Reference:
Section 14.3 of Statistics by Agresti and Franklin;
Chapter 13 of Introduction to the Practice of Statistics by Moore and McCabe;
Chapter 29 of Stats: Data and
Lecture 15
Wed, Feb. 27
36-202
Spring 2013
Continuing Two-Way ANOVA
[Reserve Material Reference:
Section 14.3 of Statistics by Agresti and Franklin;
Chapter 13 of Introduction to the Practice of Statistics by Moore and McCabe;
Chapter 29 of Stats: Data an
Lecture 16
Mon, Mar. 4
36-202
Spring 2013
Finishing Two-Way ANOVA
[Reserve Material Reference:
Section 14.3 of Statistics by Agresti and Franklin;
Chapter 13 of Introduction to the Practice of Statistics by Moore and McCabe;
Chapter 29 of Stats: Data and
Lecture 17
Wed, Mar. 6
36-202
Spring 2013
Introduction:
Repeated-Measures ANOVA
(a.k.a. within-subjects ANOVA)
This document is available on: http:/www.cmu.edu/blackboard (under course content)
Statistics Lecture 17
Page 2 of 10
Example 1:
Two algebra pla
Lecture 18
Mon, Mar. 18
36-202
Spring 2013
Finishing One-Way Repeated-Measures ANOVA
(a.k.a. within-subjects ANOVA)
Repeated-measures (within-subjects) ANOVA is where the same subjects get measured
k times (i.e., where the same subjects are exposed to all
Lecture 19
Wed, Mar. 20
36-202
Spring 2013
Two-Factor Repeated-Measures (within subjects) ANOVA
and
Two-Factor Mixed (within-between subjects) ANOVA
Last time, we discussed repeated measures (within-subjects) ANOVA, in the case of a
single explanatory fac
Lecture 20
Fri, Mar. 22
36-202
Spring 2013
Finishing Two-Factor Mixed (within-between subjects) ANOVA
Example: Comparing Two Advertising Campaigns for Athletic Shoes.*
* This example comes from chapter 29 of Applied Linear Statistical Models by Netter, et
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