Statistics and Data Science 363

Introduction to Statistical Methods

Spring 2019

Course Home Page



Course Syllabus 

Course Textbook


Resource Webpage

Homework Guidelines



Introduction to Statistical Methods (DATA363) is the foundation course for the Statistics and Data Science undergraduate major and minor.



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.



·      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.



Day-to-Day 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.




office hours


Joe Watkins

jwatkins at

Tuesdays 11:00AM-12:00PM

Fridays 12:00PM-1:00PM

Fridays 1:00PM-2:00PM

522 Mathematics

522 Mathematics

220 Mathematics

Rhoda Muse

rmuse at

Mondays 10:00AM-12:00PM

Wednesdays 1:00PM-2:00PM

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 in-class 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 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





check points








midterm exams








final exam








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