Welcome to the Age of Data, where information is all around us, helping us live happier, healthier lives. Or does it? Do we know yet if cell phones cause cancer? Have we come to a decision on whether we should be eating lots of meat or none at all to stay healthy? Despite all of this information, it can be challenging to turn it into the knowledge from which we can make sound decisions.
Statistics is the field that aims to bridge this gap between information and knowledge and this course is an application-oriented introduction to modern statistical modeling and inference. We will discuss topics such as: study design, descriptive statistics, data visualization, random variables, probability and sampling distributions, point and interval estimates, hypothesis tests, resampling procedures, multiple regression, and Bayesian models. A wide variety of applications from the natural and social sciences will be used.
Class Meeting Times
- Section #1: MWF 11:00-11:50 in LIB 204
- Section #2: MW 13:10 - 14:30 in LIB 204
- Section #3: MW 14:40 - 16:00 in LIB 204
Professors
Section #1: Tom Allen (he/him/his)
- Office: Elliot 408
- Office Hours: Mon 0930-1030 and Fri 1200-1300 or by appointment
Sections #2 & 3: Heather Kitada (she/her/hers)
- Office: Library 394
- Office Hours: Tuesday 10:00 am - 12:00 pm and Wednesday 11:00 am - 12:00 pm and 4:00 - 5:00 pm
Evening Help Session
- TBA
Textbook
OpenIntro: Introductory Statistics with Randomization and Simulation (2014), by Diez, Barr and Çetinkaya-Rundel, available in three formats: pdf, tablet-friendly pdf, and paperback edition. The textbook is free and open-source, but you’re encouraged to purchase the paperback edition through Amazon for < $10. The textbook is a key component of the course.
Class components
This course has three components: Problem Sets, Labs, and Exams/Quizzes. For details on the first two, see the tabs at the top of the page.
Exams
We’ll have several examinations and quizzes throughout the semester in order to challenge your understanding and provide us with a sense of where you’re at. Some will be more traditional pen and paper and others are to be done with the computer using R.
Midterm I
Date TBA
Midterm II
Date TBA
Final
Finals Week
Collaboration on exams is prohibited unless otherwise stated.
Grades
Problems on homeworks and exams will be graded according to the following scale:
+ Perfect
✓+ Minor mistakes
✓ Major mistake, right idea
✓- Wrong but contains a significant idea
- Wrong but contains a relevant idea
0 None of the above
NG Not graded
In addition to this scheme critical feedback will be provided to each student to aid in their learning process.
Your course grade will be based on your performance on the homework, exams, a project, and class participation. It is expected that you actively engage in conversations in class by asking questions and participating in classroom discussions.
Academic honesty
While collaboration is valuable to the learning experience you must write your own solutions independently. Your written work should honestly represent your understanding of the material. You should acknowledge your collaborators and tutors by listing their names at the start of your solution The internet is a great source of information about mathematics; you are welcome to search information about the material of the course online, but you should not search for solutions to specific problems in the homework. The following are considered violations of the honor policy: cheating, fabrication, assisting, tampering, and plagiarism
Technology in the classroom:
Computers will be used for a portion of the class to teach R statistical programming. While computers are being used for in class activities with R, students will be asked to stay on task and refrain from social media, browsing the internet, checking email, etc. In general, the use of electronic devices (computers, cell phone, tablets, etc) is prohibited during all other times because it is distracting for both the instructor and fellow students. If you have a specific reason for needing to use technology (for example, note taking) please let me know.
Disability Support Services
Accommodations are collaborative efforts between students, faculty and Disability Support Services department. Students who believe they are eligible for accommodations but who have not yet obtained approval through DSS should contact DSS immediately at 503-517-7921.