Mathematics
Statistics
and Data Science 363
Introduction
to Statistical Methods
Spring 2019
Course
Home Page
Introduction to Statistical Methods (DATA363) is the foundation course for the Statistics and Data Science
undergraduate major and minor.
Objectives.
We shall be using your background in the
natural or social sciences, the humanities, or engineering and your previous
knowledge of algebra, calculus and linear algebra to consider the issues of collection, model derivation and
analysis, interpretation, explanation, and presentation of data. The objective this course is to take
advantage of the coherent body of knowledge provided by statistical theory having
an eye consistently on the application of the subject. This approach will allow
you to extend your ability to use methods in data science beyond those given in
the course.
The major prerequisites are comfort with
calculus and a strong interest in questions that can benefit from statistical
analysis. Willingness to engage in
explorations utilizing statistical software is an important additional
requirement. The breadth of our examples will show that statistics is applicable to
a wide variety of academic disciplines, from the natural and social sciences to
the humanities and engineering.
Goals
·
leave students capable of understanding
what statistical thinking is and how to integrate this with scientific
procedures and quantitative modeling and
·
learning how to ask
statistics experts productive questions, and how to implement their ideas using
statistical software and other computational tools.
DaytoDay Operations.
The class meets Tuesdays and Thursdays from
9:30 to 10:45 AM in room 318 of the Richard A. Harvill
Building. The schedule of topics, the course textbook, and
assignments are given in the course syllabus.
This course is taught using a flipped format. Thus, you will be
typically listening to 5 to 6 short lectures, answering 2 to 3 checkpoint exercises and submitting responses the night before class.
The class will begin with a discussion to assess your understanding of the
lectures and to discuss the assignment. Most of the class will be devoted to
using a worksheet having one or two statistics or probability problems. So, you
will need to bring a laptop to class.
Much of the work can be completed during class
time and you are encouraged to work together during class. The worksheets, which are also submitted
through D2L, are due in at the
beginning of the subsequent class.
Participating in the course and attending
lectures and other course events are vital to the learning process. As such, attendance is required at all lectures and
discussion section meetings. Absences may affect a student's final course
grade. If you anticipate being absent, are unexpectedly absent, or are unable
to participate in class online activities, please contact me as soon as
possible.
instructor 
email 
office hours 
location 
Joe
Watkins 
jwatkins
at math.arizona.edu 
Tuesdays 11:00AM12:00PM Fridays 12:00PM1:00PM Fridays 1:00PM2:00PM 
522 Mathematics 522 Mathematics 220 Mathematics 
Rhoda Muse 
rmuse at math.arizona.edu 
Mondays 10:00AM12:00PM Wednesdays 1:00PM2:00PM 
370EE ENR2 370EE ENR2 
Feel free to stop by our offices in the Mathematics Building or in the Environmental and Natural Resources Building 2
(ENR2), or send an email.
Use of Software.
We will do some software computation using
R. R is a free software environment for statistical
computing and graphics. It compiles and runs on a wide variety of UNIX
platforms, Windows and MacOS. To download R, please choose your preferred CRAN
mirror. As with any computer software, the syntax in R will seem awkward at
first. Many of you will also want to download Rstudio, which is also free. Rstudio provides a
graphical user interface that will make the use of R go more smoothly.
Copies of Introductory
Statistics with R by Peter Dalgaard are available at the bookstore. Other options
for software assistance can be found on the resource webpage.
Evaluation of Students.
·
The checkpoints are short exercises described in the videos and are
meant to solidify your understanding of a concept. These are due in the D2L Assignments midnight the
evening before class. Check point
exercises are graded based on honest
effort.
·
The worksheets are generally 1 or 2 problems with several parts that
are designed to deepen and integrate your knowledge. We will start on the
worksheet problems in class. You will often need to complete the worksheet
after class. These are due at the
beginning of the following class. Students are encouraged to work together,
and consequently students are expected
to attend class. Everyone is expected to turn in their own assignment. Worksheets will be marked according to
the following guidelines.
Permission in writing to turn in late homework
for credit must be arranged in advance.
·
Students will also design and complete
a project that
analyzes data using statistical software.
·
We shall have 2 inclass midterm exams and a
comprehensive final
exam. Our final is scheduled for
Tuesday, May 7th, 2018 from 8:00 am to 10:00 am.
Students who are unable to attend an exam should notify the
instructor as soon as possible. Arrangements for a makeup test will be
considered on a casebycase basis. Makeup exams will be administered only at
the discretion of the instructor at a mutually arranged time. Failure to contact the instructor will result
in a grade of zero on the exam.
We will keep the top 22 checkpoint scores and 20 worksheet
scores. In addition, we will have ~5 optional sections on more advanced topics
(indicated in red). Check point exercises for these
sections will be counted as extra credit.
The grading scheme is
number 
points 
total 

check points worksheets 
22 20 
5 10 
110 200 
midterm exams 
2 
100 
200 
project 
1 
65 
65 
final exam 
1 
175 
175 
total 
750 
Grades will be given on the usual scale A is 90%100%, B is
80%89%, C is 70%79%, D is 60%69%, and E is below 60%. The instructor may move these cutoff values down.
Any grading disputes must be addressed within one week after an exam or
homework has been returned. If you fail to complete the course due to
circumstances unforeseen, then you may qualify for a grade of I, incomplete
in accordance with University Policy.
Honors Contracts.
This course is
available for honors contract. Students
who elect sign an honors contract will be given a project to complete in lieu
of some of the written homework assignments, and the project grade will replace
the corresponding homework grades. To
negotiate the details of an honors contract for this course, please contact the
instructor within the first week of classes.
Students should take the time to become familiar with policies and codes
of the University, notably the academic
integrity policy and student
code of conduct. Student with special needs should contact SALT  Strategic Alternative Learning
Techniques Center or the Disability
Resources Center.
Best wishes to you for a good semester in this course and in
all your other activities.
 Joe Watkins