Textbook

Stat2: Building Models for a World of Data (2014), A. R. Cannon, G. W. Cobb, B. A. Hartlaub, J. M. Legler, R. H. Lock, T. L. Moore, A. J. Rossman, J. A. Witmer, W. H. Freeman and Company.

Statistical Packages & Computing

R will be used extensively 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 programs will be placed in P:\Data\Math\Hartlaub\DataAnalysis. Proper maintenance of computer accounts, files, etc. is your responsibility.

Our class meets in a computer equipped classroom, and we will be using statistical software extensively in the course. During regular class hours, the use of computers is restricted to students enrolled in the course. Furthermore, the use of the computers is also restricted to activities deemed appropriate by the instructor. 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 the instructor and other students in the course. 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.

Lead Tutor

Grant Carney (carneyg@kenyon.edu) will be working with us as your lead tutor. He will be available to help you with your R scripts and answer other conceptual questions during evening hours on Sunday, Tuesday, and Thursday. He is not responsible for doing your homework exercises, but he will help you with questions related to choosing, fitting, assessing, and using statistical models. The can be very technical questions about how to get R to do something that you want to do or general conceptual questions.

Learning Disabilities and Math Anxieties

If you have a disability and feel that you may have need for some type of academic accomotation(s) in order to participate fully in this class, please feel free to discuss your concerns with me in private and also self identify yourself to Erin Salva, Coordinator of Disability Services at PBX 5453 or via e-mail at salvae@kenyon.edu.

Homework

Homework assignments will be given throughout the semester. I encourage you to work on as many problems as possible, including problems which have not been assigned. Subsets of the homework assignments will be collected and graded. Working with other students is encouraged, but each student must submit her/his own solution for problems to be collected.

Late Policy

Your work must be turned in 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 or send e-mail to hartlaub@kenyon.edu before class.

Exams

Quizzes

Short or take home quizzes will be given occasionally throughout the semester. 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 quizzes will take approximately 20 minutes.

Small Group Project

You will be asked to solve a practical data analysis problem with at least one other member of the class. A written component (paper or poster) and/or an oral presentation to the class will be required. The deadlines and more detailed instructions on the project will be announced in class.

Final Project

Each student will find a data set and apply an appropriate analysis. Ideally, this data set will be one which you collect yourself or obtain from a local resource. That is, I encourage you to design and conduct your own experiment. The variables in the data set and the purpose of the study must be clearly defined. If the data is obtained from a periodical, the date of publication must be later than January 1, 2010. Summaries of your proposed analysis must be submitted on or before Monday, December 2. Final papers explaining the problem of interest, your analysis, and your conclusions must be submitted on or before Tuesday, December 17at 4:30 pm.

Grades

Your course grade will be based on your overall percentage. The categories used to determine your overall percentage and their respective weights are listed below.

Class participation will be used to help make borderline decisions.

Course Outline

• Chapter 0 - What is a Statistical Model?
• Chapter 1 - Simple Linear Regression
• Chapter 2 - Inference for Simple Linear Regression
• Chapter 3 - Multiple Linear Regression
• Chapter 4 - Additional Topics in Regression
• Chapter 5 - Analysis of Variance (ANOVA)
• Chapter 6 - Multifactor ANOVA
• Chatper 7 - Additional ANOVA Topics
• Chapter 9 - Logistic Regression
• Chapter 10 - Multiple Logistic Regression
• Chapter 11 - Additional Topics in Logistic Regression