Elements of Statistics

Stat 106

Spring 2023

Professor Bradley A. Hartlaub
Office 305 Rutherford B. Hayes Hall
Phone 740-427-5405
Office Hours

Required Text

OpenIntro Statistics (2019), 4th Edition, David M. Diez, Christopher D. Barr, and Mine Cetinkaya Rundel - see our Google Drive folder Stat106-ElementsofStatistics-F2023\!OpenIntroTextbook
Learning Goals Statistical Package and Computing

The statistical software package RStudio willl be used throughout the course. Assignments and course announcements will be sent to you via e-mail or posted on the course web page. Data sets and R scripts will be placed in our Google Drive Folder Stat106-ElementsofStatistics-S2023. Proper maintenance of computer accounts, files, etc. is your responsibility. I recommend that you back up your data sets and R scripts on a regular basis. I will not assume you have prior experience with statistical software so you do not need to be concerned about the use of technology in the classroom.

Our class meets in a classroom where you will be expected to use your laptop, and we will be using statistical software extensively in the course. The use of our laptop is also restricted to activities deemed appropriate for this course. Playing computer games, reading e-mail, conversing in a chat room, surfing the web, and working on assignments for other courses are a few examples of inappropriate activities that can be distracting to other students in the course and the instructor. Inappropriate computer use may result in penalties ranging from warnings to loss of computer privileges for the period. In cases of extreme and/or repeated violations, grade penalties or expulsion from the course may result.

Accessibility Accomodations

A student who thinks they may need an accommodation to access a campus program, activity, or service should contact Ruthann Daniel Harteis in Student Accessibility and Support Services (SASS) at danielharteis1@kenyon.edu  to discuss specific needs. Advance notice is required to review documentation, evaluate accommodation requests and provide notice or arrangements for any accommodation.

Title IX Responsibilities

As a member of the Kenyon College faculty, I am concerned about the well-being and development of students, and am available to discuss any concerns. However, I want you to know that faculty members are legally obligated to share certain information with the College’s Civil Rights & Title IX Coordinator. This requirement is to ensure your safety and welfare is being addressed. These disclosures include, but are not limited to: reports of discrimination or harassment due to a protected characteristic, including sexual harassment, sexual assault, relational/domestic violence, and stalking.

Homework & Activities
Homework and activities will be given throughout the semester. Subsets of these assignments will be collected and randomly selected questions will be graded frequently. You should work on as many problems as possible. This includes problems which have not been assigned. All papers that you turn in must be legible with problem numbers and solutions clearly marked. I encourage you to discuss the concepts and problem solving techniques presented in class with other students. However, you must submit your own solution for each of the assigned problems to be collected. Your lowest homework score will be dropped and the remaining scores will be averaged to obtain your homework percentage. For more infomation, see the departmental guidelines for collaboration on homework, which I expect you to follow.


Tutors will be available on Sunday, Tuesday, and Thursday evenings. They will help you with technical software questions or general questions about the course material, but they will not solve your homework problems.
Late Policy
Assignments must be turned in at the beginning of the class period on the assigned due date. No credit will be given for late papers. If for any reason you cannot turn in your paper on the assigned date, you must contact me before class. If you are unable to contact me, you can leave a message on my office phone (740-427-5405) or send e-mail to hartlaub@kenyon.edu.

Attendance Policy:

In relation to the Kenyon Class Attendance Policy and The Department of Mathematics and Statistics Attendance Policy, nine class absences (whether excused or unexcused) will result in expulsion from the course.


  • Exam 1 - Friday, February 24
  • Exam 2 - Friday, April 14
  • Comprehensive Final Exam - Tuesday, May 9, 6:30 - 9:30 pm
  • Quizzes

    Quizzes will be given frequently throughout the course on Friday between major exams. The goal is to help you comprehend and apply the important concepts and techniques that we have been studying in a timed setting. In other words these quizzes are designed to help you prepare and practice for the exams. The lowest quiz score will be dropped and the remaining quiz scores will be averaged.

    Short Papers

    You and your lab partner(s) must submit at least two short papers (2-4 pages in length).  Each paper should contain a discussion of a current news item whose understanding requires a knowledge of statistics or probability.  Excellent sources of information for these short papers are journal articles in a particular field of interest to you. The date of publication for your article should be within the last 5 years. You may submit one draft for my feedback before submitting the final draft to be graded.  The only major requirement is that you submit at least one paper by March 3 and at least two papers by April 21. The Electronic Journal Center and JSTOR are also excellent sources of information for these short papers.

    Your course grade will be based on your overall percentage. The categories used to determine your overall percentage are listed below with their respective weights.
  • Homework and Activities (15%)
  • Short Papers (10%)
  • Quizzes (25%)
  • Exam 1 (15%)
  • Exam 2 (15%)
  • Final Exam (20%)
  • Class participation will be used to help make borderline decisions.
    Course Outline
  • Chapter 1 Introduction to data
  • Chapter 2 Summarizing data
  • Chapter 3 Probability
  • Chapter 4 Distributions of random variables
  • Chapter 5 Foundations for inference
  • Chapter 6 Inference for categorical data
  • Chapter 7 Inference for numerical data
  • Chapter 8 Introduction to linear regression
  • Chapter 9 Multiple and logistic regression (if time permits)