STAT 11 prepares students to think statistically and to be able to perform basic statistical analyses using the statistical software package R. Topics include exploratory data analysis, design of surveys and experiments, hypothesis testing and confidence intervals, and an introduction to linear regression. Applications will be drawn from interdisciplinary case studies spanning the humanities, social sciences, public policy, medicine, and the sciences. This course is intended for students who want a practical introduction to statistical methods.
Upon successful completion of the course, students are expected to be able to:
We will be using the 5th edition of Stats: Data and Models.You are required to have access to Pearson’s MyLab. You can purchase an 18 week access package through the campus bookstore.
We will also use the MyLab Statistics that accompanies our Pearson eText. Links to this lab will be provided on our course Moodle page. You will also work with software to perform statistical analyses in this class. You may use either Excel (or Google sheets) or RStudio.
If you wish to use RStudio, you may access Swarthmore’s RStudio Server through your browser (this requires internet access and Swarthmore VPN if off-campus). Alternatively you may download the software to run RStudio on your personal computer (this does not require internet access but requires an initial time commitment to set up correctly). To download RStudio on you own computer you must first download the base software R. Then, once you have completed the installation of base R, you can download the free RStudio user interface.
Typically there are three course meetings per week and class attendance is mandatory. Class meetings will be held in the Science Center room 145. Course meetings will combine: theoretical statistical knowledge and applied examples, applications using R, and in-class exercises/discussions. To be best prepared for class please bring with you a laptop and a note-taking device. Please don’t be a distraction to those around you. If you do not have access to a laptop, tell your professor who can arrange for you to borrow one. The textbook is meant to serve as an accompaniment to your in-class notes and you are expected to prepare for class by reading the weekly assigned chapters.
Each week, you will be assigned homework practice problems to be completed in the Pearson myLab. These assignments are meant to be completed by yourself alone to practice your own understanding of the topics covered in your assigned reading. Each assignment consists of \(10\) questions with a total of \(15\) homework assignments. You do not need to get perfect scores on any of them. Your grade for these assignments is entirely based upon your completion of the assignments by December 13, 2023 at 11:59pm. A non-zero percentage complete for each assignment is all that is needed for you to get a perfect grade on this course component. Do not wait until the end of the semester to get started on these. No extensions whatsoever will be given for the reading comprehension assignments.
Your attendance in class is mandatory. You are encouraged to ask questions and engage with the lesson and you will spend weekly class time working in small randomly chosen groups. Occasionally, your professor will record classroom attendance. After your second unexcused absence, your course grade will be penalized \(1\%\) point for each subsequent unexcused absence. The way to determine if an absence is excused is by communicating with your professor.
Over the semester, you will be assigned \(5\) homework project problem sets. Each problem set will each be graded for correctness by a course teaching assistant. You are encouraged to work with one another one these homework assignments although you are responsible for handing in your own copy of the solutions on-time. You may use either Excel or RStudio to analyze data in these assignments. Your homework project grade component will be the average score of all \(5\) assignments. Late homework project submissions will receive a grade of \(0\).
There will be three in-class quizzes emphasizing the most recent material covered in class. These quizzes are closed book but you may be permitted to use a single, handwritten “cheat sheet” according to specifications set forth by your professor. If you have a scheduling conflict due to a religious holiday or travel plans on a quiz day, you must let me know at least two weeks ahead of time. Otherwise, no make-up quizzes will be permitted.
There will be one group project that you will present on the last week of class. The goal of this project is to gain hands-on experience collecting and analyzing data while corroborating with other group mates to practice your verbal statistical literacy. You will have the option to be assigned to a group or to work with your own group. Group size is limited to \(4\) students per group.
The final exam will take place on December 20th from 9am-12pm in room SCI 183. Makeups will not be permitted for the final exam.
Your entire course grade is determined by each course component listed above and your attendance in class. If there are no penalties accrued from unexcused absences then homework assignments will account for \(25\%\) of your overall grade (reading comprehension assignments count for \(15\%\) and project assignments count for \(10\%\)), each of the three in-class quizzes will account for \(10\%\) of your overall course grade, your group final project will account for \(25\%\) of your overall course grade, and the final exam will account for \(20\%\) of your overall course grade.
You can estimate your course grade at any time using the following formula: \[0.15(\text{reading_comp_completed}) + 0.10(\text{hw_project_average}) + 0.10(\text{quiz1} + \text{quiz2} + \text{quiz3}) + 0.25(\text{final_project}) + 0.20(\text{final_exam}),\] where each grade component is some number between \(0\) and \(100\). To convert your numeric grade to a letter grade, I will use the following:
| Numeric grade | Letter grade |
|---|---|
| \(\geq 97\) | A\(+\) |
| \([95, 97)\) | A |
| \([90, 95)\) | A\(-\) |
| \([87, 90)\) | B\(+\) |
| \([85, 87)\) | B |
| \([80, 85)\) | B\(-\) |
| \([77, 80)\) | C\(+\) |
| \([75, 77)\) | C |
| \([70, 75)\) | C\(-\) |
| \([67, 70)\) | D\(+\) |
| \([65, 67)\) | D |
| \([60, 65)\) | D\(-\) |
| \(< 60\) | F |
Statistics is a powerful tool for scientific discovery and communication. Statistics instructors play an important role in equipping new practitioners with the knowledge, skills, and abilities to practice statistics ethically and transparently. As such, it is your professor’s duty to provide you with an honest, accurate assessment of your current abilities throughout the semester. Your assessments (and grades) are designed to inform you so you are confident about what you have mastered and so you can pinpoint where you have the most room for growth and improvement. Your grades do not determine your future success in statistics nor are they reflective of your general intelligence. Nevertheless, your grades provide indispensable feedback that is integral towards supporting the widespread practice of ethical, transparent, and reproducible statistics. By remaining enrolled in this course you agree that it is your duty to hold yourself to the highest standards of academic integrity.
Quizzes and exams must be completed by yourself without communicating to others and all work must be your own. Cheating, copying, etc. will not be tolerated. Upon the first offense, all parties involved will receive a zero grade and must schedule a subsequent in-person meeting with your professor. Failure to meet with your professor within two weeks of the offense will result in having your case filed with the College Judiciary Committee. Upon a second offense, a case will be filed with the College Judiciary Committee for all parties involved. If you are unsure of whether or not your actions are complying with the assignment/quiz/exam instructions, ask for clarification. If you are feeling desperate enough to consider cheating, contact your professor and explain what is going on so you can get additional support.
We all come to class with different backgrounds and experiences and this diversity of thought and perspective will enrich our learning environment. Respect for one another’s identities and contributions to class discussions is mandatory. This includes attempting to avoid using diminutive language that is not uncommon to hear within other mathematics settings. Although you may hear phrases such as "that’s trivial", "it’s obvious that", "clearly…", or "it’s intuitive that" in many math and statistics courses, your professor makes a conscious effort to avoid this language in her lectures and she expects you to avoid using this language in her classroom too. What is obvious to one person could be another person’s PhD thesis and what is “intuitive” is entirely subject to one’s own experiences. These efforts from you and your professor will help maintain a supportive learning environment where everyone feels comfortable participating and questions are encouraged and not looked down upon.
Your professor will solicit explicit feedback from you twice throughout the semester. Each time, the feedback forms will be identical and anonymous. The first time your feedback is requested will be about halfway through the semester. Your feedback here helps your professor identify what is and isn’t working for you and to make reasonable adjustments accordingly. The last time your professor requests your feedback is at the end of the semester. You are welcome to submit feedback before or after the final exam. This feedback helps your professor prepare for and improve upon future sessions of this course.
As you may know, this type of feedback, although informative, is heavily biased. Not only is volunteer response bias inevitable, there are studies that indicate strong emotional and social biases influence course evaluations based on identity factors such as the instructor’s race, gender, sexuality, and age and based on external factors that influence respondents’ emotions related to the course material. (See, for example [1] and [2], among others.) You may wish to read over this quick guide on how to avoid bias in your course evaluation responses.
If you find that you have more feedback and ideas that you’d like to share with the department at large, please consider joining one of the Math/Stat department student groups. Currently these groups consist of Gender Minorities in Math/Stat (GeMS), Black in Math/Stat (BIMS), and the Math/Stat Student Advisory Council (MSSAC). Participation in these groups is a great way to communicate with the Math/Stat faculty and staff directly as we work together to build a more equitable learning environment for all students. Feel free to ask your professor about how to get involved!
In-person office hours are located in SCI 136 and will be held each W and F from 10:20am - 11:20am. Each week, your professor is available for an addition office hour via Zoom at https://swarthmore.zoom.us/my/sthornt1. Any student wishing to utilize this virtual office hour must email your professor at least 24 hours in advance to schedule a visit. You are expected to attend office hours as often as you need.
During office hours, your professor will try to make sure that everyone who shows up gets a chance to ask a question. Typically, student questions are prioritized according to whoever arrives first but sometimes your professor may prioritize questions that are shared by more than one student. When it’s your turn to ask a question, please limit yourself to only one question (even if it is a question with a very short answer). Once everyone present has gotten a chance to ask their question, you will get the opportunity to ask another question. When others are asking questions, pay attention because you might learn something from the discussion.
Stat Clinics are drop-in study sessions run by friendly and knowledgeable upperclassmen every Sunday-Wednesday night 7-10 pm in SC 158. Clinics are a wonderful opportunity to study, do homework, meet/work with classmates, and ask questions about statistics and math. Because clinics are drop-in, you are welcome to come and go as you please. Please write your name and course in the sign-in binder and the time you enter and leave the clinic so the Math/Stat Department has a record of your attendance. To make the most of your time at clinic, be sure to first try problems on your own, or bring questions you have from your text or lecture. Having your textbook, lecture notes, and online resources handy is essential because these are helpful resources for both you and the Clinician working with you. There will likely be other students at Clinic with questions for the Clinician, so do not expect to get individual attention the entire time you are at clinic. Be open to working on other problems, thinking about and trying to work through the question you have for the Clinician, working with classmates, or doing other coursework while you wait to speak with the Clinician.
| Stat 11 Clinic | SC 158 from 7-10pm |
|---|---|
| Su | Cici Wen |
| M | Joyce Ben, Omar Khan |
| Tu | Ben Horvat |
| W | Atesh Camurdan |
In addition to our traditional drop-in stat clinics, students now have the opportunity to schedule an individual 30 minute meeting with a clinician. One-on-one clinics are offered for Stat 1, 11, and 21 students only on Monday or Thursday night 7-10 pm.
| Stat 11 One-on-One Clinic | SC 142 (or Zoom) from 7-10pm |
|---|---|
| M | David Yang |
| Th | Michael Eddy |
Students have the option to submit a topic they would like help with ahead of time or email a draft of their work to the clinician. Alternatively, students may sign up and bring their course materials and questions directly to the meeting. Meetings may take place in person in SC 142 (the small computer lab) or on zoom. Priority is given to students who sign up in advance.
For questions about Math & Stat Clinics please visit https://www.swarthmore.edu/math-stat-academic-support/math-and-stat-clinics or contact Laura Dandridge ldandri1@swarthmore.edu, Academic Support Coordinator for the Math/Stat Department. Laura also has office hours to directly support students working on Stat 1, Stat 11, or PreCalculus material in preparation for Math 15/25, and you may sign up for an appointment on WASE.
Your professor will not provide individual accommodations unless you provide her with proof of registration with Student Disability Services (SDS). Accommodations require early planning and are not retroactive.
If you believe you need accommodations for a disability or a chronic medical condition, you may contact SDS via email at studentdisabilityservices@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, the office will issue students with documented disabilities or medical conditions a formal Accommodations Letter. For details about the accommodations process, visit the Student Disability Services website.
Swarthmore provides support to students who are experiencing academic stress, difficult life events, or feelings of anxiety or depression through the Counseling and Psychological Services (CAPS). If you or someone you know needs support please call 610-328-7768 to speak with a licensed counselor at any time or visit the CAPS website to schedule an appointment.
Part of why it is important to your professor to maintain an inclusive and respectful classroom is because it’s incredibly difficult to "do math" under stress. For example, give this game a try! If you are experience stress inside the classroom, your professor is here to help you. If you are experiencing stress outside the classroom, CAPS is here to help you.
Please be aware that all faculty are “responsible employees”, which means that if you a professor about a situation involving sexual harassment or assault, dating violence, domestic violence, or stalking, they must share that information with the Title IX Coordinator. Although professors are responsible for this notification, you have complete control over how your case will be handled, including whether or not you wish to meet with the Title IX coordinator or pursue a formal complaint.
[1] Fan Y, Shepherd LJ, Slavich E, Waters D, Stone M, Abel R, et al. (2019) Gender and cultural bias in student evaluations: Why representation matters. PLoS ONE 14(2). doi:10.1371/journal.pone.0209749.
[2] Mitchell, K., & Martin, J. (2018). Gender Bias in Student Evaluations. PS: Political Science & Politics, 51(3), 648-652. doi:10.1017/S104909651800001X.