Math 3680.001 and Math 3680.002: Fall 2012
3680.001 Meets: TR 8:00-9:20 in Discovery Park, Room
B185.
3680.002 Meets:
TR 12:30-1:50 in Curry Hall, Room 204.
Instructor: Professor John
Quintanilla
Office: GAB, Room 418-D
Office Phone: x4043
E-mail: jquintanilla@unt.edu
Web page: http://www.math.unt.edu/~johnq/Courses/2012fall/3680/
Office Hours: TR 10-12, or by appointment. I'm fairly easy to find,
and you're welcome to drop by outside of office hours without an appointment.
However, there will be occasions when I'll be busy, and I may ask you to wait
or come back later.
Online Help: Dr. Quintanilla or a
teaching assistant will be available online through Enhanced WebAssign at the following times:
Tuesdays: 10-12 (Dr. Quintanilla)
Wednesdays: 1-3 (Di Wu), 3-5 (Jose
Islas)
Thursdays: 8-10 (Jose Islas), 10-12 (Dr.
Quintanilla)
Fridays: 9-10:30 (Rachid
Atmai), 1-3 (Di Wu), 3-5 (Rachid
Atmai)
To get help online, click Communication near the top of the
Enhanced WebAssign page and then follow the prompts.
The teaching assistants will be available beginning in September.
Required Text: Probability & Statistics for
Engineering and the Sciences, by J. L. Devore. There are two options
for purchasing this text. The second option is cheaper; however, this only
provides temporary online access to the textbook, so that you would neither be
able to use a physical hard copy of the book this semester nor permanently add
it to your bookshelf after completing the course.
·
Bundle: Text + Enhanced WebAssign
- Start Smart Guide for Students + Enhanced WebAssign
Homework with eBook Printed Access Card for One Term Math and Science. Can be
purchased for $160 at the UNT Bookstore.
·
eBook + Enhanced Web
Assign. Can be purchased for $73.75 at the UNT Bookstore.
Strongly Recommended: Lecture notes for the semester can be purchased
from Eagles Images (located in the 2nd floor of the University Union) for
$18.95.
Technology: You will be expected
to bring to class --- including exams --- either a laptop computer with a
spreadsheet program (such as Microsoft Excel or Open Office Calc)
or else a calculator that can perform multiple statistical functions. In class,
I will demonstrate how to use Microsoft Excel and a TI-83 Plus to perform
various statistical functions. If you have some other kind of calculator, you
are welcome to ask me before or after class about how to use its statistical
functions.
Course Description: Descriptive statistics, elements of probability,
random variables, confidence intervals, hypothesis testing, regression,
contingency tables.
Prerequisite: Math 1710 and Math 1720 (may be taken concurrently).
What You Should Do Immediately
Please read the Enhanced WebAssign handout,
distributed on the first day of class, for instructions about how to enroll
yourself in the appropriate section of Math 3680. You will need the Class Key
Code given at the top. Be sure to enroll in Section 001 if your class is at
Discovery Park in the morning, but enroll in Section 002 if your class is in
Curry Hall in the afternoon.
Click here
for further instructions about getting started with Enhanced WebAssign.
I strongly encourage you to get started with Enhanced WebAssign
as soon as possible. If you delay, you run the risk of unforeseen technical
problems that could prevent you from completing the first assignments (both due
on Friday, September 7, with a bonus possible if submitted by September 5).
While Enhanced WebAssign is required for the
course, it is my understanding that, at the start of the semester, you have a
14-day grace period to use Enhanced WebAssign for
free. After this grace period, a code must be entered to continue to use
Enhanced WebAssign.
Course Topics
The following chapters and sections of the textbook will be
covered according to the projected schedule below. Dates may change as events
warrant.
- Chapter 1: Overview
and Description Statistics
- 1.1 Populations,
Samples and Processes
- 1.2 Pictorial and
Tabular Methods in Descriptive Statistics
- 1.3 Measures of
Location
- 1.4 Measures of
Variability
- Chapter 2: Probability
- 2.1 Sample Spaces and
Events
- 2.2 Axioms,
Interpretations, and Properties of Probability
- 2.4 Conditional
Probability
- 2.5 Independence
- Chapter 3: Discrete
Random Variables and Probability Distributions
- 3.1 Random Variables
- 3.2 Probability
Distributions for Random Variables
- 3.3 Expected Values
- 3.4 The Binomial
Probability Distribution
- 3.5 Hypergeometric and Negative Binomial Distributions
- Chapter 4: Continuous
Random Variables of Probability Distributions
- 4.1 Probability
Density Functions
- 4.2 Cumulative Distribution
Functions and Expected Values
- 4.3 The Normal
Distribution
- 4.6 Probability Plots
- Chapter 5: Joint
Probability Distributions and Random Samples
- 5.4 The Distribution
of the Sample Mean
- 5.5 The Distribution
of a Linear Combination
- Chapter 7: Statistical
Intervals Based on a Single Sample
- 7.1 Basic Properties
of Confidence Intervals
- 7.2 Large-Sample
Confidence Intervals for a Population Mean and Proportion
- 7.3 Intervals Based
on a Normal Population Distribution
- Chapter 8: Test of
Hypotheses Based on a Single Sample
- 8.1 Hypotheses and
Test Procedures
- 8.2 Tests About a
Population Mean
- 8.3 Tests Concerning
a Population Proportion
- 8.4 P-Values
- Chapter 9: Inferences
Based on Two Samples
- 9.1 z Tests and Confidence Intervals
for a Difference Between Two Population Means
- 9.2 The Two Sample t
Test and Confidence Interval
- 9.3 Analysis of
Paired Data
- 9.4 Inferences
Concerning a Difference Between Population Proportions
- Chapter 12: Simple
Linear Regression
- 12.2 Estimating Model
Parameters
- 12.5 Correlation
- Chapter 13: Nonlinear
and Multiple Regression
- 13.2 Regression with
Transformed Variables
- Chapter 14:
Goodness-of-Fit Tests and Categorical Data Analysis
- 14.1 Goodness-of-Fit
Tests When Category Probabilities Are Completely Specified
- 14.3 Two-Way
Contingency Tables
August 30
|
Lecture
#1
|
1.2, 1.3, 1.4
|
Graphical
Representation of Data
|
Page 3: Box and
Whisker
Page 7: Histogram
|
September 4
|
Lecture
#2
|
1.3, 1.4
|
Mean
and Standard Deviation
|
Pages 2-3: Trimmed
Means
Page 5: Mean and SD
|
September 6
|
Lecture
#3
|
2.2, 2.4
|
Probability:
Axioms and Multiplication Rule
|
Page 2: Probability
Page 5:
Multiplication Rule
Page 7: Tree Diagram
|
September 11
|
Lecture
#4
|
2.2, 2.5
|
Probability:
Independence and Addition Rule
|
Page 2: Independence
Page
3A: Multiplication Rule
Page 3B:
Parallel/Series
Page 8A: Deck of
Cards
Page 8B: Venn Diagram
Page 9: Venn Diagram
|
September 13
|
Lecture
#5
|
3.1,
3.2, 3.3
|
Discrete
Random Variables and Probability Distributions
|
Page 2: Cumulative
Distribution Function
Page 5: Mean and SD
|
September 18
|
Lecture
#6
|
3.4,
3.5
|
Binomial
and Hypergeometric Distributions
|
Pages 5-6: Binomial
Page 9: Hypergeometric
|
September 20
|
Lecture
#7
|
4.1,
4.2
|
Continuous
Random Variables
|
Page 2: Probability
and Cumulative Distribution Function
Page 3: Percentile
Page 5: Mean and SD
|
September 25
|
Lecture
#8
|
4.3
|
The
Normal Distribution
|
Page 4: Probability
Page 5: Percentile
|
September 27
|
Exam #1
|
Chapters 1-3
Lectures 1-6
|
Review
#1
The exam
will consist of 21 multiple-choice questions. In addition to the above
review, I have provided the following sample so
that you’re accustomed to this format on the day of the exam.
Comments on Exam #1
|
The videos below give the solutions to each of the
review exercises. I encourage you to attempt each problem on your own
before watching the videos.
1.1 1.2 1.3 1.4 1.5 1.6,7,8
1.9,10,11 1.12,13 1.14 1.15
|
October 2
|
Lecture
#9
|
4.3,
5.4
|
Approximating
Bin(n,p) with the Normal Distribution
|
Page 4: Normal
Approximation of Binomial Distribution
|
October 4
|
Lecture
#10
|
4.6,
5.5
|
Probability
Plots and Linear Combinations of Random Variables
|
Page 2: Probability
Plot
|
October 9
|
Lecture
#11
|
5.4
|
The
Central Limit Theorem
|
Page 6: Estimating
Probability Involving a Sum
|
October 11
|
Lecture
#12
|
7.1,
7.2
|
Confidence
Intervals: Large samples or known s
|
Page 7: Two-Sided
Confidence Interval for a Population Mean
|
October 16
|
Lecture
#13
|
7.2
|
Confidence
Intervals: One-Sided for Means and Two-Sided for Proportions
|
Page 3: One-Sided
Confidence Interval for a Population Mean
Page 6: Two-Sided
Confidence Interval for a Proportion
|
October 18
|
Lecture
#14
|
7.3
|
Confidence
Intervals and Prediction Intervals: Small Samples
|
Page 3: t Distribution
Page 5: Two-Sided
Confidence Interval for a Population Mean (Small Sample)
Page 7: Prediction
Interval
|
October 23
|
Lecture
#15
|
8.1
|
Introduction
to Hypothesis Testing
|
To be added later
|
October 25
|
Exam #2
|
Chapters 4-7
Lectures 7-14
|
Review
#2
Comments on Exam #2
|
The videos below give the solutions to each of the
review exercises. I encourage you to attempt each problem on your own
before watching the videos.
2.1 2.2 2.3 2.4 2.5
2.6 2.7 2.8 2.9 2.10
2.11 2.12 2.13 2.14-15
2.16 2.17
Note: In 2.11, I discuss how to find critical values for
the t distribution using a table.
Here are the solutions from the
review given in class on October 18.
|
October 30
|
Lecture
#16
|
8.2
|
Hypothesis
Testing: The z-Test
|
Page 1: Right-tailed
z-Test
Page 7: Type II Error
Page 9: Sample size
for a given value of b
|
November 1
|
Lecture
#17
|
8.2
|
Hypothesis
Testing: The z-Test and t-Test
|
Page 1: Left-tailed
z-Test
Page 2: Type II Error
and Sample Size
Page 4: Two-tailed
z-Test
Page 6: Right-tailed
t-Test
Page 9: Two-tailed
t-Test
|
November 6
|
Lecture
#18
|
8.3
|
Hypothesis
Testing: The z-Test and Proportions
|
Pages 4-5: Right-tailed
z-Test for a Proportion, Type II Error, and Sample Size
|
November 8
|
Lecture
#19
|
8.4
|
P-values
|
Page 1: Right-tailed
z-Test
Page 5: Left-tailed
t-Test
|
November 13
|
Lecture
#20
|
9.1
|
Two-Sample
Data: Unpaired Large Samples
|
Pages 1 and 5:
Hypothesis test for the difference in the averages of two large samples
Page 6: Confidence
intervals for the difference in the averages of two large samples
|
November 15
|
Lecture
#21
|
9.2,
9.3, 9.4
|
Two-Sample
Data: Unpaired Small Samples and Proportions
Note:
The slides for Section 9.3 can be found here;
they were inadvertently omitted from the lecture notes.
|
Page 1: Hypothesis
test for the difference in the averages of two small samples
Page 7: Hypothesis
test for the difference of two proportions
|
November 20
|
Lecture
#22
|
12.5
|
Correlation
|
To be added later
|
November 22
|
NO CLASS: HAPPY THANKSGIVING
|
November 27
|
Lecture
#23
|
12.2,
13.2
|
Linear
and Intrinsically Linear Regression
|
Page 8: Intrinsically
Linear Regression: Percolation
Page 9: Intrinsically
Linear Regression: Planets
|
November 29
|
Exam #3
|
Chapters 8-9
Lectures 15-21A
|
Review
#3
Comments on Exam #3
|
The videos below give the solutions to each of the
review exercises. I encourage you to attempt each problem on your own
before watching the videos.
3.1 3.2 3.3 3.4 3.5
3.6 3.7 3.8 3.9 3.10
3.11 3.12 3.13 3.14
3.15 3.16 3.17
|
December 4
|
Lecture
#24
|
14.1,
14.3
|
The
Chi-Squared Distribution
|
Page 8: Specified
Proportions
Page 9: Testing Independence
|
December 6
|
Review
|
Chapters 12-14
Lectures 22-24
|
Review
#4
|
The videos below give the solutions to each of the
review exercises. I encourage you to attempt each problem on your own
before watching the videos.
4.1 4.2 4.3 4.4
4.5 4.6 4.7
|
December 11, 8-10 am
|
Final for
Math 3680.001
|
December
13,
10:30
am-12:30 pm
|
Final for
Math 3680.002
|
|
|
Student Responsibilities
- Student behavior that interferes
with an instructor's ability to conduct a class or other students'
opportunity to learn is unacceptable and disruptive and will not be
tolerated in any instructional forum at UNT. Students engaging in
unacceptable behavior will be directed to leave the classroom and the
instructor may refer the student to the Center for Student Rights and
Responsibilities to consider whether the student's conduct violated the Code of Student Conduct. The
university's expectations for student conduct apply to all instructional
forums, including university and electronic classroom, labs, discussion
groups, field trips, etc.
- You should read over
this syllabus carefully, as I will hold you responsible for the
information herein.
- Students will be
expected to read the chapters carefully, including the examples in the
book.
- Students will be
responsible for obtaining any and all handouts. If you are not in class
when handouts are given, it is your responsibility to obtain
copies.
- You should begin working
now.
Frequent practice is crucial to the successful completion of a mathematics
course. Cramming at the last minute will certainly lead to failure.
- WARNING: If you are in academic
trouble, or are in danger of losing your financial support, or if your
parent or guardian is expecting a certain grade at the end of the
semester... start working today. I will refuse to listen to any pleas at
the end of the semester. You will receive precisely the grade that you earn.
Grading Policies
You may find the advice
of former Math 3680 students helpful.
The following schedule is tentative and is subject to capricious changes in
case of extracurricular events deemed sufficiently important to the upper
administration.
Final Exam
|
Section 1: December 11,
8-10 am
Section 2: December 13: 10:30
am – 12:30 pm
|
25%
|
Exam 1
|
c. Week 4
|
20%
|
Exam 2
|
c. Week 8
|
20%
|
Exam 3
|
c. Week 12
|
20%
|
Homework
|
|
15%
|
|
|
|
|
|
A
|
90% and above
|
B
|
80% and below 90%
|
C
|
70% and below 80%
|
D
|
60% and below 70%
|
F
|
below 60%
|
|
Cooperation is encouraged in doing the homework assignments. However, cheating
will not be tolerated on the exams. If you are caught cheating, you will be
subject to any penalty the instructor deems appropriate, up to and including
an automatic F for the course. Refer to the following university site for
the official policy with regards to academic dishonesty: http://vpaa.unt.edu/academic-integrity.htm.
Attendance is not required for this class. However, you will be responsible
for everything that I cover in class, even if you are absent. It is my
experience that students who skip class frequently make poorer grades than
students who attend class regularly. You should consider this if you don't
think you'll be able to wake up in time for class consistently.
The grade of "I" is designed for students who are unable to
complete work in a course but who are currently passing the course. The
guidelines are clearly spelled out in the Student Handbook. Before you
ask, you should read these requirements.
Exam Policies
- I expect to give exams
on the days shown above. However, these are tentative dates. I will
announce the exact date of each exam in class.
- You
will be expected to bring to class --- including exams --- either a laptop
computer with a spreadsheet program (such as Microsoft Excel or Open
Office Calc) or else a calculator that can
perform multiple statistical functions. I strongly encourage you to recharge the battery of your laptop or
calculator the night before the exam. Also, if you’re bringing
your laptop, you may wish to also
bring a power strip, as electrical outlets are not plentiful in the
classroom.
- After exams are returned
in class, you have 48 hours to appeal your grade. I will not listen to any
appeals after this 48-hour period.
- I will not drop the
lowest exam score; all will count toward the final grade.
- Students missing an exam
for unauthorized reasons will receive 0 (zero) points on the exam.
Students will be required to provide official written verification
of any authorized absences.
- The Final Examination
will be comprehensive in the sense that problems may come from any of the
sections that will be covered during the semester.
- The grade of A signifies
consistent excellence over the course of the semester. In
particular, an A on the final is not equivalent to an A for the course.
- I reserve the right to
test and quiz you on problems which are generalizations of material
covered in the class and/or in the text. In short, the problems may not
look exactly like the ones in the book.
- Everything that I say in
class is fair game for exam material. You will be responsible for
everything unless I advise you to the contrary.
Homework Policies
o
Each
part of each exercise can be attempted up to 10 times. In other words, you
could submit answers to part (a) of Exercise #1 up to 10 times, and then you
could move on to attempt part (b).
o
Your
last submission will count as your final answer.
o
You
can save your work without using a submission.
o
Some
exercises will use randomization. In other words, it’s possible that
every student will have slightly different questions with accordingly different
answers.
o
Homework
will be due every Friday at 11:59 pm.
o
A
5% bonus will be awarded to students who complete their homework more than 48
hours before the due date.
o
If
requested no more than a week after the original due date (i.e., by the
following Friday at 11:59 pm), it is possible to receive an automatic extension
on homework through Enhanced WebAssign. Any work done
after the automatic extension can be submitted for half credit as long as it
completed within 24 hours of the request.
- When computing grades, I
will drop the two lowest homework grades before computing the
homework average. Therefore, in principle, you could get a 100% homework
score and also not turn in two assignments during the semester. I have
this policy in case you get sick, a family
emergency arises, etc., during the semester. You will still be responsible
for the material in such assignments during the examinations.
- With the exception of
the automatic extensions noted above, I will not give extensions on
homework assignments (called manual
in Enhanced WebAssign), nor will I accept late
assignments.
Note to TNT Students
- If you’re pursuing
secondary teacher certification through Teach North Texas, then you may be
aware that you will be required to construct a preliminary teaching
portfolio in EDSE 4500 (Project-Based Instruction) and a final portfolio
during your final semester of student teaching. Section 2 of this
portfolio will ask you to demonstrate your knowledge of your content
field. You may find that some of the assignments may naturally become
artifacts toward part of this task, and so I encourage you to keep your
work after the semester is over to make the eventual construction of your
portfolio easier. You may even want to write (and save for later) a brief
reflection on the artifact you select, rather than try to remember why the
artifact you chose was important once you reach EDSE 4500.
- The specific indicators
in the portfolio related to knowledge of mathematical content are as
follows:
- Reflect on one or more
artifacts in which you state a mathematical theorem or conjecture and
apply both formal and informal mathematical reasoning to the same
conjecture.
- Reflect on one or more
artifacts that show your ability to describe a mathematical concept that
can be represented in multiple ways and articulate the connections
between its representations in clear, expository prose. Where relevant,
identify appropriate technology for exploring the concept and explain
limits the technology may place on the knowledge acquired.
- Reflect on one or more
artifacts that show your ability to generate a model of a natural
phenomenon or describe an already existing model and evaluate how well
the model represents the situation, including consideration of the risks,
costs, and benefits of the alternatives.
- Reflect on one or more
artifacts that show your ability to identify a topic in your subject area
and describe its connection with prerequisite topics, future topics, and
other subjects.
- Reflect on one of more
artifacts that show how you bring out the historical and cultural
importance of your subject material, its contribution to large ideas, and
its significance in today’s society. Include a specific lesson plan
that incorporates the general history and cultural context of modern
science or of mathematics as these fields have evolved.
- Just to be clear: the
above is a suggestion for TNT students. This is NOT a course requirement
for Math 3680.
Final Note
The University of North Texas makes reasonable academic
accommodation for students with disabilities. Students seeking accommodation
must first register with the Office of Disability Accommodation (ODA) to verify
their eligibility. If a disability is verified, the ODA will provide you with
an accommodation letter to be delivered to faculty to begin a private
discussion regarding your specific needs in a course. You may request
accommodations at any time, however, ODA notices of accommodation should be
provided as early as possible in the semester to avoid any delay in
implementation. Note that students must obtain a new letter of accommodation
for every semester and must meet with each faculty member prior to
implementation in each class. For additional information see the Office of
Disability Accommodation website at http://www.unt.edu/oda. You may also
contact them by phone at 940.565.4323.