Class Resources
Syllabus
COVID-19 related changes to the course structure are noted in blue below. However, given the changing nature of the current situation, the syllabus may be subject to further revision.
Course Overview
Course goals & objectives:
This course introduces students to the discipline of statistics as a science of understanding and analyzing data. Throughout the semester, students will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.
The course goals are as follows:
- Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
- Use statistical software to summarize data numerically and visually, and to perform data analysis.
- Have a conceptual understanding of the unified nature of statistical inference.
- Apply estimation and testing methods to analyze single variables or the relationship between two variables in order to understand natural phenomena and make data-based decisions.
- Model numerical response variables using a single or multiple explanatory variables.
- Interpret results correctly, effectively, and in context without relying on statistical jargon.
- Critique data-based claims and evaluate data-based decisions.
- Complete a research project demonstrating mastery of statistical data analysis from exploratory analysis to inference to modeling.
Course structure:
The course is divided into seven learning units. For each unit a set of learning objectives and required and suggested readings, videos, etc. will be posted on the course website. You are expected to watch the videos and/or complete the readings and familiarize yourselves with the learning objectives. We will begin the unit with a readiness assessment: 10 multiple choice questions that you answer using your clickers in class. You will then re-take this assessment in teams. The rest of the class time will be split between discussion of the material and application exercises that you’ll complete in teams. Slides and other relevant materials for each class and lab will be available on the schedule page before each class. Videos and other relevant prep materials for each unit are also available on that page. Within each unit you will complement your learning with problem sets and labs, and wrap up each unit with a (optional) performance assessment.
Graded Components
Grading Breakdown:
| PRE-CHANGE Participation in Application Exercises, Class Attendance, and Peer Evals | 5% |
|---|---|
| POST-CHANGE Participation in Application Exercises and Peer Evals | 5% |
| Problem sets | 10% |
| Readiness assessments-Individual | 7.5% |
| Readiness assessments-Group | 7.5% |
| Labs | 10% |
| Project | 10% |
| Midterm 1 | 12.5% |
| Midterm 2 Takehome | 12.5% |
| Final Takehome | 20% |
Grades may be curved at the end of the semester. Final grades of a C- or better will receive a Satisfactory (S) and grades of a D+ or lower will recieve an Unsatisfactory (U).
If you choose to receive a letter grade for this class, you can do so by submitting a form to the registrar's office no later than April 22 at 5:00 pm EST. Your final grade will be calculated using the rubric above (also with potential curving). This will be the same curve given to the rest of the class.
Attendance, participation, and peer evaluation:
Application Exercises:The main way of getting participation credit for the rest of the semester will be via the application exercises. Like the begining of the semester, during the lecture (TuTh 11:45 EST) you will get together in groups to work on the Application Exercises and ask for help from me and the TAs. Unlike the beginning of the semester, your group must get the application exercise 100% correct before the beginning of the next lecture to get credit for it.
Peer Evaluations:Throughout the semester you will also be asked to complete a few peer evaluations. These will be used to ensure that all team members contribute to the success of the group and to address any potential issues early on. If you feel that there are issues within your team, you are encouraged to discuss it with your team members and to bring it to my or your TA’s attention.
Problem sets:
The problem set submission process will be the same as before.These will be assigned (approximately) weekly on the course webpage and will be comprised of problems from the textbook. Each assignment will list roughly five to seven problems from the book to be turned in for grading, and roughly 10 practice problems. You do not need to turn in the practice problems, and the solutions can be found in the back of the book.
The objective of the problem sets is to help you develop a more in-depth understanding of the material and help you prepare for exams and the project. Grading will be based on completeness as well as accuracy. In order to receive credit you must show all your work.
You are welcomed, and encouraged, to work with each other on the problems, but you must turn in your own work. If you copy someone else’s work, both parties will receive a 0 for the problem set grade as well as being reported to the Office of Student Conduct. Work submitted on Sakai will be checked for instances of plagiarism prior to being graded.
Submission instructions: You will turn in your problem sets on Sakai. We strongly recommend working in a word processor of your choice (Word, Google Docs, etc.), saving your work as PDF, and submitting the PDF. This will ensure that what we read is exactly what you intended to submit. You are also welcomed to submit your document as a Word file as well, however if we cannot open your file you will receive a 0 on the problem set (so it is worth taking that risk!). Alternatively, you can type your answers in the text box in Sakai however if you lose internet connection and you haven’t saved, you might lose your work (so if you’ll use this approach make sure to save often!).
All assignments will be time stamped and late work will be penalized based on this time stamp (see late work policy below).
Lowest score will be dropped.
Readiness assessments:
General:Readiness assessments will be given at the beginning of a unit. These are 10 question multiple choice assessments comprised of conceptual questions addressing the learning objectives of the new unit. You are not expected to master all topics in the unit ahead of time, but you are responsible for completing the reading assignment, understanding how the material fits in the greater framework of the course, and acquire a conceptual understanding of the learning objectives.
How to Do Online:
Two lowest scores will be dropped.
Labs:
The lab group submission process will be the same as before. You will collaboratively work on these labs using Zoom during the lab times in your groups (with assistance from your lab TA). Please see the STA101 Sakai main page for your lab zoom links.The objective of the labs is to give you hands on experience with data analysis using modern statistical software. The labs will also provide you with tools that you will need to complete the project successfully. We will use a statistical analysis package called RStudio, which is a front end for the R statistical language.
In class your TAs will give a brief overview of the lab and learning goals, and guide you through some of the exercises. You will start working on the lab during the class session, but note that the labs are designed to take more than just the class time, so you will meet up with your team at a later time to finish the lab before the due date (which will be the following lab session).
With each lab you will also be asked to note team members attendance in lab session and percentage contribution. Students are allowed 2 excused absences where they can complete the work on their own, or with their team (if there's work left for them to do), without the receiving a penalty. After 2 excused lab absences, team members who fail to attend a lab section (for any reason) but who still contribute to the weeks lab report will lose 30 pts on the lab. Email Dr. Ellison if you miss more than 2 labs. Team members who both don't attend lab and also do not contribute to that weeks lab report will not be eligible for any points on that lab. Team members who contribute less than 10% on lab assignment may lose 20 points on the lab.
Submission instructions: Always submit the Rmd and the HTML files for your lab. One submission per team.
Lowest score will be dropped.
Exams:
The second midterm and the final exam will be take-home exams that should be completed INDIVIDUALLY and submitted on Sakai. You will have a 24 hours to complete these exams, and they will be emailed out roughly on the date/time EST listed in the schedule. There will MANY different versions of Midterm 2 and the Final Exam. On the date/time EST that we have our exam, you will receive an email from me giving sending you a copy of your personalized exam. You should submit your answers to this exam on Sakai before the deadline. For every hour that the exam is submitted past the deadline, 30 points will be deducted from the exam.There will be two midterms and one final in this class. See course info for dates and times of the exams. Exam dates cannot be changed and no make-up exams will be given.
You are allowed to use the notes/resources/book/lecture slides/application exercises to complete this exam. However, you are not allowed to get help from other students/me/the TAs/your friends/your dog/your cat/your fish/your parents/your siblings/or anyone else but you!
Project:
The project will be completed and presented in a similar fashion to the original in-person class. For the rest of the semester, you should set aside time (as a group) to work on the project. You can set up personal Zoom group meetings to work on this. On the second to last lab day of the semester, you will be able to work on this lab during your lab session with assistance from me and your lab TAs. On the last lab day you will submit your final group project rmd file and html file, and you will attend your labs and present your project as a group over Zoom.The objective of the project is to give you independent applied research experience using real data and statistical methods. You will complete the semester long project in teams. There will be a mid-checkpoint where you write a proposal for your research direction and present results from exploratory data analysis. At this stage you will also describe your collaborative approach outlining each team member’s past and planned contribution and a plan for how the work will come together.
Note that each student must complete the project and score at least 30% of total possible points on each project in order to pass this class.
Team members will provide feedback on percentage contribution to the final product, and grades for each student will be determined based on the quality of the product and their contribution to the work. If everyone contributes equally, all members will get full credit. Team members who do not contribute sufficiently will be deducted points.
Course Requirements and Resources
Book:
OpenIntro Statistics, 3rd Edition: https://www2.stat.duke.edu/courses/Spring20/sta101.002/openintro-statistics-edition-3.pdf Textbooks for this course are available for 3-hour checkouts at the Duke Libraries. Search the Libraries' Top Textbooks program here.
Clickers:
You don't need your iClickers anymore. We will conducting polls over Zoom and will record "attendance" in a similar way.Throughout the lectures you will use Zoom polls to both answer conceptual questions and for data collection/class surveys.
Calculator:
(optional) (just something that can do square roots)
Teams:
To construct highly functional teams of learners, you are asked to complete a short survey as well as a pre-test to gauge your previous exposure to statistics and statistical literacy. If you haven’t yet done so please complete these items as soon as possible.
You will be assigned to teams of 3-5 students based on the results of the survey and the pre-test. Once team assignments have been made there is no option for changing teams, other than under extraordinary circumstances. You will work in these teams during application exercises and team portions of the readiness assessments. In addition, your team members will be your first point of contact in this class.
You are encouraged to study with your team members and other classmates. But remember that anything that is not explicitly a team assignment must be your own work.
Coursera Videos and Learning Objectives:
The videos that you will need to watch to prepare for the readiness assessments are hosted on the Duke/Coursera page. Do the following to access these videos.
- Go the course schedule and click on the listed video links in the the corresponding "Unit #" row in "Materials" column.
- After clicking on this link, enter the following credentials and click "Log in."
- Email: firstname.lastname@duke.edu
- Password: your_duke_password
- Watch all the videos that correspond to this week.
- Watching the videos for the stipulated "Coursera weeks" is all you need to do. You don't need to take the online quizzes, but they can be VERY useful in preparing for the Readiness Assessments.
Sakai
- Problem Set submission (individual)
- Lab Assignment submission (one person from each group submits)
- Project proposal submission (one person from each group submits)
- Take Performance Assessment Quiz (individual) )
- View Grades and Solutions/Answers
Piazza Forum:
I would highly suggest using Piazza more for questions you might have, now that we have switched to an online format!Any non-personal questions related to the material covered in class, problem sets, labs, project, etc. should be posted on Piazza. Before posting a new question please make sure to check if your question has already been answered. The TAs and myself will be answering questions on the forum daily and all students are expected to answer questions as well. Please use informative titles for your posts.
Note that it is more efficient to answer most statistical questions ``in person" so make use of office hours.
Access Piazza by clicking on the Piazza link on the Sakai course website.
DukeHub
https://dukehub.duke.edu/
Class grades posted here.
Other learning resources:
Aside from the your TAs’ and the professor’s office hours, you can also make use of the Academic Resource Center. The Academic Resource Center (ARC) offers free services to all students during their undergraduate careers at Duke. Services include Learning Consultations, Peer Tutoring and Study Groups, ADHD/LD Coaching, Outreach Workshops, and more. Because learning is a process unique to every individual, we work with each student to discover and develop their own academic strategy for success at Duke. Contact the ARC to schedule an appointment. Undergraduates in any year, studying any discipline can benefit! 211 Academic Advising Center Building, East Campus, behind Marketplace arc.duke.edu * theARC@duke.edu * 919-684-5917
RStudio
We will be performing our lab data analysis assignments using R and using R-studio as an interface.To start a VM R-Studio environment go here: https://vm-manage.oit.duke.edu/containers
Then click on the link that says 'Click here to log in(create) to your R-Studio environment', and log on using your NetID and password.
Additional Course Information
Tips for success:
- I will regularly send announcements by email, please make sure to check your email daily.
- Complete the reading before a new unit begins, and then review again after the unit is over.
- Be an active participant during lectures and labs.
- Ask questions - during class or office hours, or by email. Ask me, your TAs, and your classmates.
- Do the problem sets - start early and make sure you attempt and understand all questions.
- Start your project early and and allow adequate time to complete it.
- Give yourself plenty of time time to prepare a good cheat sheet for exams. This requires going through the material and taking the time to review the concepts that you’re not comfortable with.
- Do not procrastinate - don’t let a unit go by with unanswered questions as it will just make the following unit’s material even more difficult to follow.
- For more tips, see Course Tips
Work load:
You are expected to put in about 4-6 hours of work / week outside of class. Some of you will do well with less time than this, and some of you will need more.
Students with disabilities:
Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Student Disability Access Office at (919) 668-1267 as soon as possible to better ensure that such accommodations can be made.
Academic integrity:
Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Cheating on exams and quizzes, plagiarism on homework assignments and project, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Duke Community Standard, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved as well as being reported to the Office of Student Conduct. Additionally, there may be penalties to your final class grade. Please review the Duke’s Academic Dishonesty policies.
Excused Absences:
Students who miss graded work due to a scheduled varsity trip, religious holiday or short-term illness should fill out an online NOVAP, religious observance notification, or short-term illness notification form respectively.
If you cannot complete an assignment on the due date due to a short-term illness, you have until noon the following day to complete it at no penalty. Then the regular late work policy will kick in. Make sure you send a STINF to Dr. Ellison as well as an email to Dr. Ellison and Joe Mathews (head TA) explaining the situation.
If you are faced with a personal or family emergency or a long-range or chronic health condition that interferes with your ability to attend or complete classes, you should contact your academic dean’s office. See more information on policies surrounding these conditions here. Your academic dean can also provide more information.
Policies:
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Late work policy for the problem sets and labs reports:
- next day: lose 30% of total possible points
- later than next day: lose all points
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Late work policy for the project: 10% off for each day late.
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There will be no make-ups for readiness or performance assessments, labs, problem sets, project, or exams. If the midterm exam must be missed, absence must be officially excused in advance, in which case the missing exam score will be imputed using the final exam score. This policy only applies to the midterm. All other missed assessments will receive a grade of 0. The final exam must be taken at the stated time. You must take the final exam and turn in the project in order to pass this course. Please refer to Duke policy regarding missed final exams. https://trinity.duke.edu/undergraduate/academic-policies/final-exams-scheduling-conflicts-and-absences
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Regrade requests must be made within one week of when the assignment is returned, and must be submitted in writing. These will be honored if points were tallied incorrectly, or if you feel your answer is correct but it was marked wrong. No regrade will be made to alter the number of points deducted for a mistake. There will be no grade changes after the final exam.
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Clickers may not be shared, and the clicker registered to a person may only be used by that person. Failure to abide by this will result in a 0 clicker grade for everyone involved.
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Use of disallowed materials (textbook, class notes, web references, any form of communication with classmates or other persons, etc.) during exams will not be tolerated. This will result in a 0 on the exam for all students involved, possible failure of the course, and will be reported to the Office of Student Conduct. If you have any questions about whether something is or is not allowed, ask me beforehand.