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:
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 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 performance assessment.
All videos and learning objectives as well as learning supplementary materials that you will need for preparing for the readiness assessments are hosted on Coursera. For further details refer to Resources.
| Attendance | 5% |
| Application Exercises | 5% |
| Problem sets | 10% |
| Labs + peer evaluation | 10% |
| Readiness assessments | 10% |
| Performance assessments | 5% |
| Project | 15% |
| Midterm | 15% |
| Final | 25% |
Grades may be curved at the end of the semester. The more evidence there is that the class has mastered the material, the more generous the curve will be.
Guaranteed Grading Scale: College Board Grading Scale
You are expected to put in about 10-15 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.
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.
You are expected to be present at class meetings and actively participate in the discussion. Your attendance and participation during class will make up a non-insignificant portion of your grade in this class. While I might sometimes call on you during the class discussion, it is your responsibility to be an active participant without being called on.
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 attention.
These will be assigned 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 to the practice problems 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 projects. Grading will be based on completeness as well as accuracy. In order to receive credit, you must show all your work.
You are 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 OR submit the paper copy at the beginning of class the day it’s due. If you use Sakai then we strongly recommend working in a word processor of your choice, saving your work as PDF, and submitting that. You are welcomed to submit Word files as well. However, if we cannot open your file you will receive a 0 on the problem set (so it may not be worth taking that risk!). Alternatively, you can scan your written assignment as a PDF, although you will receive a 0 on the problem set if it is illegible.
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.
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.
During lab, I 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 may need to finish the lab outside of class.
Submission instructions: Always submit the Rmd and the HTML/PDF files for your lab.
Lowest score will be dropped.
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. As described above, you will first take the individual readiness assessment and then re-take the same assessment in teams using scratch-off sheets. Your performance on both assessments will factor into your final grade (3/4 individual score, 1/4 team score). In addition, your input during the team portion will factor into your participation grade.
Performance assessments will be given at the end of a unit. These are very similar to the readiness assessments in format, except that you will be taking them outside of class on Sakai. Outstanding performance will require mastery of all topics in the unit.
Lowest score will be dropped.
The objective of the project is to give you independent applied research experience using real data and statistical methods. The project will be completed in teams.
Further details on the project will be provided later in the term.
Note that you must complete the projects and score at least 30% of total possible points on the projects in order to pass this class.
There will be one midterm 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. If you can’t take the exams on these dates you should drop this class. You can’t pass the class if you do not take the final exam. You are allowed to bring one sheet of notes (``cheat sheet”) to the midterm and the final. This sheet must be no larger than 8 1/2 x 11, and must be prepared by you. You may use both sides of the sheet.
I will regularly send announcements by email, please make sure to check your email daily.
Any non-personal questions related to the material covered in class, problem sets, labs, projects, etc. should be posted on Piazza. Before posting a new question please make sure to check if your question has already been answered. I 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 OH.
Aside from office hours, you can also make use of the Academic Resource Center.
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.
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 projects, 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.
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.
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.
Late work policy for project: 10% off for each day late.
There will be no make-ups for readiness or performance assessments, labs, problem sets, project, or exams. If the midterm exam must be missed, the 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.