Math 3680.002: Fall 2015
Meets: TR 12:30-1:50 in Sage 176.
Instructor: Professor
John Quintanilla
Office: GAB, Room 418-D
Office Phone: x4043
E-mail: There are three ways to reach me by e-mail.
1.
My usual e-mail
address: jquintanilla@unt.edu
.
2.
Through Enhanced Webassign: click Communication
near the top of the Enhanced WebAssign page and then
follow the prompts.
3.
Through Enhanced WebAssign: when doing your homework, click Ask Your Teacher near the top of the Enhanced WebAssign
page and then follow the prompts. If you
have a question about a specific homework problem, this is perhaps the best way
to communicate with me, as I can see both your message and your previous
attempts at doing your homework.
Web page: http://www.math.unt.edu/~johnq/Courses/2015fall/3680/
Office Hours: Mondays and Wednesdays 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.
Required Text:
Probability & Statistics for Engineering and the Sciences, 9th
edition, 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. Both can be purchased at http://www.cengagebrain.com/course/site.html?id=1-1MH23VQ.
·
ePack:
Probability and Statistics for Engineering and the Sciences, 9th + Enhanced WebAssign Instant Access for Statistics, Single-Term
Courses. ISBN 978-1-305-77938-9. $259.95.
·
Enhanced WebAssign Instant Access for Statistics, Single-Term
Courses, 1st Edition. ISBN 978-1-285-85804-3. $79.
Strongly Recommended: Lecture notes for the semester can be purchased from
the Eagle Images Print Center for approximately $20. The Eagle Images Print
Center is in room 124 of the University Service Building (USB), which located
near the Fouts Field Parking Lot. This is not a
convenient location, but offering them for sale elsewhere would have increased
the price of the lecture notes significantly. The
Mean Green (stop
7 on the map) and Campus
Cruiser shuttles both stop at USB. You should enter through the
north door (that is, the door that isn’t facing Fouts
Field) to easily get to the Print Center.
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
To get started with Enhanced WebAssign, visit http://www.webassign.net/manual/WA_Student_Quick_Start.pdf.
In particular, you will need to visit www.webassign.net
and use the following Class Key Code: unt 9426 3655
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 4, with a bonus
possible if submitted by September 2).
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 25
|
Lecture #1
|
1.2,
1.3
|
Graphical Representation of Data
|
Page
3: Box and Whisker Plots
|
August 27
|
Lecture #1
|
1.3,
1.4
|
Graphical Representation of Data
|
Page
7: Histograms
|
September 1
|
Lecture #2
|
1.3,
1.4
|
Mean and Standard Deviation
|
Pages
2-3: Trimmed Means
Page
5: Mean and SD
|
September 3
|
Lecture #3
|
2.2,
2.4
|
Probability: Axioms and Multiplication Rule
|
Page
2: Probability
Page
5: Multiplication Rule
Page
7: Tree Diagram
|
September 8
|
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 10
|
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 15
|
Lecture #6
|
3.4, 3.5
|
Binomial and Hypergeometric Distributions
|
Pages
5-6: Binomial
Page
9: Hypergeometric
|
September 17
|
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 22
|
Lecture #8
|
4.3
|
The Normal Distribution
|
Page
4: Probability
Page
5: Percentile
|
September
24
|
Exam #1
|
Chapters
1-3
Lectures 1-6
|
Review #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
|
September 29
|
Lecture #9
|
4.3, 5.4
|
Approximating Bin(n,p) with
the Normal Distribution
|
Page 4: Normal
Approximation of Binomial Distribution
|
October 1
|
Lecture #10
|
4.6, 5.5
|
Probability Plots and Linear Combinations of Random
Variables
|
Page
2: Probability Plot
|
October 6
|
Lecture #11
|
5.4
|
The Central Limit Theorem
|
Page 6: Estimating
Probability Involving a Sum
|
October 8
|
Lecture #12
|
7.1, 7.2
|
Confidence Intervals: Large samples or known s
|
Page 7: Two-Sided
Confidence Interval for a Population Mean
|
October 13
|
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 15
|
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 20
|
Lecture #15
|
8.1
|
Introduction to Hypothesis Testing
|
To be
added later
|
October
22
|
Exam #2
|
Chapters
4-7
Lectures
7-14
|
Review #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.
|
October 27
|
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
|
October 29
|
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 3
|
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 5
|
Lecture #19
|
8.4
|
P-values
|
Page
1: Right-tailed z-Test
Page
5: Left-tailed t-Test
|
November 10
|
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 12
|
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 17
|
Lecture #22
|
12.5
|
Correlation
|
To be
added later
|
November 19
|
Lecture #23
|
12.2, 13.2
|
Linear and Intrinsically Linear Regression
|
Page
8: Intrinsically Linear Regression: Percolation
Page
9: Intrinsically Linear Regression: Planets
|
November
24
|
Exam #3
|
Chapters
8-9
Lectures
15-21A
|
Review #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
|
November
27
|
NO
CLASS; HAPPY THANKSGIVING
|
December 1
|
Lecture #24
|
14.1, 14.3
|
The Chi-Squared Distribution
|
Page
8: Specified Proportions
Page
9: Testing Independence
|
December
3
|
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
10, 10:30 am-12:30 pm
|
Final
|
|
|
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
|
Thursday,
December 10, 10:30 am-12:30 pm
|
24%
|
Exam 1
|
c. Week 5
|
20%
|
Exam 2
|
c. Week 9
|
20%
|
Exam 3
|
c. Week 14
|
20%
|
Homework
|
|
16%
|
|
|
|
|
|
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.
- You may choose not to take the final examination, under the following
rules:
- If your course average
before the final is 93.00 or higher, you will be given an A for the
course.
- If your course average
before the final is between 83.00 and 92.99, you will be given a B for
the course.
- If your course average
before the final is between 73.00 and 82.99, you will be given a C for
the course.
- If your course average
before the final is between 63.00 and 72.99, you will be given a D for
the course.
- If your course average
before the final is less than 63.00, you will be given an F for the
course.
The idea of this policy is that, if you are comfortably
above the cut-off between grades at the time of the final exam, then you can
receive the higher grade without taking the final. However, if you are too
close to the cut-off, then you need to take the final to earn the higher grade.
- 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 are suggestions 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.
Course Name: Applied Statistics-Fall
2015
Instructor Name: Quintanilla
Class Key Code: unt 9426 3655