Mathematics 363

Introduction to Statistical Methods

Fall 2017

Course Home Page

Overview.

In Introduction to Statistical Methods, we shall be using your background in the natural or social sciences, the humanities, or engineering and your previous knowledge of algebra, calculus and differential equations 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 statistical methods 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. Even though many of our examples are derived from the life sciences, 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.

Day-to-Day Operations.

The class meets Tuesdays and Thursdays from 9:30 AM to 10:45 AM in room 301 of Cesar Chavez 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. Much of the work can be completed during class time. The worksheets are due in at the beginning of the subsequent class. Typically, you will work in pairs. Students are expected to attend class and contribute to the classroom activities. So, you will need to bring a laptop to class.

instructor	email	office hours	location
Joe Watkins	jwatkins at math.arizona.edu	Monday 11:00-12:00 Wednesday 1:00-2:00 Wednesday 2:00-3:00	220 Mathematics 522 Mathematics 522 Mathematics
Ning Hao	nhao at math.arizona.edu	Mondays 2:00-3:00 Thursday 11:00-12:00 Thursday 12:00-1:00	325 ENR2 325 ENR2 220 Mathematics
Alixx Encinas	acencina at email.arizona.edu	Tuesday 11:00-1:00	513 Mathematics

Feel free to stop by our offices in the Mathematics Building or in the Environmental and Natural Resources Building 2, room S321 and S324 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 dropbox 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 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 in-class midterm exams and a comprehensive final exam. Our final is scheduled for

Tuesday, December 12th, 2017 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 make-up test will be considered on a case-by-case basis. Make-up 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