Teaching team Data Analysis and Statistical Inference Introduction ▶ Professor: Dr. Nabanita Mukherjee - nabanita.mukherjee@stat.duke.edu ▶ TAs: Sta 101 - Fall 2017 – Tessa Johnson (Head TA) – tessa.johnson@duke.edu – Kai Wang – kai.wang23@duke.edu Duke University, Department of Statistical Science – Kara McCormack – kara.mccormack@duke.edu – Xuetong Li – xuetong.li@duke.edu – Jenny Bai – jingyi.bai@duke.edu – Ahmed Ahad – ahmed.ahad@duke.edu Dr. Mukherjee Slides posted at http://www2.stat.duke.edu/courses/Fall17/sta101.002/ 1 Required materials Webpage ▶ OpenIntro Statistics, 3rd Edition: http://openintro.org/os ▶ i > clicker2 - See Google Doc for a list of students selling used clickers (link emailed) http: ▶ (optional) Calculator (just something that can do square roots) //www2.stat.duke.edu/courses/Fall17/sta101.002/ 2 3
Learning units and course outline Course structure ▶ Pictures and summaries of data – Unit 1 - Intro to data: Observational studies & non-causal inference, principles of experimental design & causal inference, exploratory data ▶ Set of learning objectives and required and suggested readings, analysis, introduction to simulation-based statistical inference. videos, etc. for each unit. ▶ Mathematics behind statistics ▶ Prior to beginning the unit, watch the videos and/or complete – Unit 2 - Probability & distributions: Basics of probability and chance processes, Bayesian perspective in statistical inference, the normal and the readings and familiarize yourselves with the learning binomial distributions. objectives. ▶ Statistical inference ▶ Begin a new unit with a readiness assessment: individual, then – Unit 3 - Framework for inference: CLT, sampling distributions, and introduction to theoretical inference. team. – Midterm 1 ▶ Class time: split between lecture, discussion/application, and – Unit 4 - Statistical inference for numerical variables – Unit 5 - Statistical inference for categorical variables lab. – Midterm 2 ▶ Modeling ▶ Complement your learning with problem sets. – Unit 6 - Simple linear regression: Bivariate correlation and causality, ▶ Wrap up a unit with a performance assessment. introduction to modeling. – Unit 7 - Multiple linear regression: More advanced modeling with multiple predictors. – Final Exam 4 5 Teams Clickers ▶ Highly functional teams of learners based on survey and pre-test. Objective: Two-way communication and instant feedback. ▶ Team members first point of contact. ▶ Application exercises, labs, team readiness assessments, ▶ Readiness assessments (graded for accuracy) project. ▶ Questions throughout lecture (graded for participation) – Get credit for the day you by responding to at least 75% of the ▶ Study together, but anything that is not explicitly a team questions. assignment must be your own work. – Up to three unexcused late arrivals or absences. ▶ Peer evaluations to ensure that all team members contribute to ▶ Register your clicker at the class 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 ASAP ( don’t wait till things get worse). 6 7
Project Exams Objective: Give you independent applied research experience using Midterm 1 Oct 3, Tue real data and statistical methods. Midterm 2 Nov 9, Thu Final Dec 17 , Sun - 7-10pm ▶ Proposal: due mid-semester ▶ Poster session: last lab of semester ▶ Exam dates cannot be changed, no make-up exams will be ▶ Complete in teams, along with peer evaluations to track given contribution of each member ▶ If you cannot take the exams on these dates you should drop ▶ Must complete the project and score at least 30% of the points this class on each project in order to pass this class ▶ Calculator + cheat sheet allowed 8 9 Email & Piazza Office Hours ▶ Prof: – Office hours: Thursdays, 12 - 2 pm, Old Chem 122A (my office) ▶ TAs: ▶ I will regularly send announcements by email, so make sure to check your email daily. TA Day / time Location Walker Harrison T 12-2pm Old Chem 211A Tessa Johnson W 10 - 11am, 11am - 12 pm Old Chem 211A/025 ▶ All content related (non-personal) questions should be posted Yixuan Wang T 2-3pm, F 9-10am Old Chem 221A Jose San Martin Th 9-10am, F 12-1 pm Old Chem 211A on Piazza. Ahmed Ahad T 4 - 5 pm, Th 6-7 pm Old Chem 025 Jenny Bai M 4-6 pm Old Chem 211A ▶ Before posting a new question please make sure to check if Xuetong Li M 11:30 am-12:30 pm, W 9 -10 am Old Chem 211A your question has already been answered, and answer others’ questions. ▶ Use informative titles for your posts. ▶ It is more efficient to answer most statistical questions “in person” so make use of OH. 10 11
Students with disabilities Academic Dishonesty Any form of academic dishonesty will result in an immediate 0 on the given assignment and will be reported to the Office of Student Students with disabilities who believe they may need Conduct. Additional penalties may also be assessed if deemed accommodations in this class are encouraged to contact the appropriate. If you have any questions about whether something is Student Disability Access Office at (919) 668-1267 as soon as or is not allowed, ask me beforehand. possible to better ensure that such accommodations can be made. Some examples: ▶ Use of disallowed materials (including any form of communication with classmates or accessing the web) during exams and readiness assessments ▶ Plagiarism of any kind http://www.access.duke.edu/students/requesting/index.php ▶ Use of outside answer keys or solution manuals for the homework 12 13 Tips for success To do ▶ 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. ▶ Download or purchase the textbook ▶ Ask questions - during class or office hours, or by email. Ask me, your TAs, – Download: http://openintro.org/os and your classmates. – Purchase: http://openintro.org/os/amazon ▶ Do the problem sets - start early and make sure you attempt and understand all questions. ▶ Obtain and register your clicker in class ▶ Take each PA and complete practice quizzes (on Coursera) for each unit, and ▶ Read the syllabus and let me know if you have any questions review the feedback for questions you miss. ▶ Watch/Read/Review the resources for Unit 1 ▶ Start your project early and and allow adequate time to complete them. – RA 1 on Thursday – not graded (for practice) ▶ 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. 14 15
Syllabus quiz (10 minutes). You can refer to the syllabus at http://www2.stat.duke.edu/courses/Fall17/sta101.002/ . 16 17
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