Statistical Inference and Data Analysis Course Overview

Data Analysis and Statistical Inference STA101-FALL 2018 Syllabus Overview https://www2.stat.duke.edu/courses/Fall18/sta101.001/

Objectives Statistics: A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. Webster s New Collegiate Dictionary Main Objective of Statistics: Make an inference about a population based on information contained in a sample from that population and to provide an associated measure of goodness for the inference. Wackerly et al. 2002 Main Objective of the Course: Make sense of data using statistical tools. Explore relationships between variables. Make informed decisions.

Course Overview Introduction to statistics as a science of understanding and analyzing data. Themes include data collection, exploratory analysis, inference, and modeling. Focus on principles underlying quantitative research in social sciences, humanities, and public policy. Research projects teach the process of scientific discovery and synthesis and critical evaluation of research and statistical arguments. Readings give perspective on why in 1950, S. Wilks said "Statistical thinking will one day be as necessary a qualification for efficient citizenship as the ability to read and write."

Class Times and Locations Main Lecture: M/W 3:05pm-4:20pm French Science 2231 Instructor: Dr. Victoria Ellison (In-class TAS: Gauri Kamat and Yunxuan Li) Labs: 1. TH 8:30am-9:45am, Perkins LINK 088 Classroom 4 (TA: Becky Tang) 2. TH 10:05am-11:20am, Perkins LINK 088 Classroom 4 (TA: McCourt Hu) 3. TH 11:45am-1:00PM, Perkins LINK 088 Classroom 4 (TA: Vittorio Orlandi) 4. TH 1:25pm-2:40pm, Perkins LINK 088 Classroom 4 (TA: Becky Tang) 5. TH 3:05pm-4:20pm, Perkins LINK 088 Classroom 4 (TA: Mark Sampson)

Contact and Office Hours Instructor: Dr. Victoria Ellison Office Hours: F 1:30-3:30 (Old Chemistry Building 208A) TAs: Becky Tang: WF 9:30am-10:30am Old Chemistry Building 203 McCourt Hu: WF 4:30pm-5:30pm Old Chemistry Building 203 Mark Sampson: W 6:00pm-8:00pm Old Chemistry Building 203 Gauri Kamat: M 8:30 am-10:30am Old Chemistry Building 203 Vittorio Orlandi: Th 4:30pm-6:30pm Old Chemistry Building 203 Yunxuan Li: TH 1:00 pm- 3:00 pm Old Chemistry Building 203 Other: Dr. Mine Cetinkaya-Rundel Director of Undergraduate Studies STA101 Course Designer, Book Author, Coursera Video Lecturer Dr. Tavis Abrahamsen STA101-002 Professor

Course Overview Pictures and summaries of data Unit 1 - Intro to data: Observational studies & non-causal inference, principles of experimental design & causal inference, exploratory data analysis, introduction to simulation-based statistical inference. Mathematics behind statistics Unit 2 - Probability & distributions: Basics of probability and chance processes, Bayesian perspective in statistical inference, the normal and binomial distributions. Statistical inference Unit 3 - Framework for inference: CLT, sampling distributions, and introduction to theoretical inference. Midterm 1 Unit 4 - Statistical inference for numerical variables Unit 5 - Statistical inference for categorical variables Midterm 2 Modeling Unit 6 - Simple linear regression: Bivariate correlation and causality, introduction to modeling. Unit 7 - Multiple linear regression: More advanced modeling with multiple predictors. Final Exam

Required for Course Items: Book: OpenIntro Statistics, 3rd Edition: http://openintro.org/os Clicker: i>clicker2 Get: Bookstore or used (See Google Doc for a list of students selling used clickers (link emailed) Register: In-class Calculator: (optional) (just something that can do square roots) Accesses (see syllabus for access information): Class website: https://www2.stat.duke.edu/courses/Fall18/sta101.001/ Sakai: https://sakai.duke.edu/portal R-Studio: https://vm-manage.oit.duke.edu/containers Piazza (on Sakai) Coursera: https://www.coursera.org/programs/duke-university-courses- gp9dy?authProvider=duke&browseProductType=COURSE&languages=en DukeHub: https://dukehub.duke.edu/ Teams

General Class Structure: Flipped Class 8 Lecture Units Unit Structure: Learning Course Material: FIRST: Student goes through primer unit videos BEFORE the unit is covered in class. SECOND: Unit discussed more in-depth during in-class lectures (Mondays/Wednesdays) along with in-class application exercises. 10 Labs: Learn technical data analysis skills related to the unit using R in labs (Thursdays).

General Unit Schedule: See course website for specific deadlines and due dates. STARTING A NEW UNIT 1. BEFORE CLASS watch corresponding unit coursera videos and corresponding unit read book sections. 2. DURING CLASS on the first day of a new unit take a Readiness Assessment (RA). a. First, individually via iclicker. Info: 10 multiple choice questions Grading Process: 75% of Readiness Assessment grade for the unit. Time: ~15 minutes b. Next, take same readiness assessment with groups, via ScratchCards. Grading Process: 25% of Readiness Assessment grade for the unit. One point for question: Correct answer scratched off and JUST ONE answer scratched off. point for question: Correct answer scratched off and JUST TWO answers scratched off. Time: ~10 minutes. c. Go over Readiness Assessment.

General Unit Schedule (contd): See course website for specific deadlines and due dates. DURING THE UNIT 1. DURING CLASS go more in-depth into unit topics. a. Lectures b. iClicker Questions a. Individual submission. b. Credit: Answers not graded. Must answer 75% of questions for class attendance credit. c. Application Exercises (AE) a. Group submission. b. Credit: Counts as class-participation credit. c. Submission: On paper to TA by the end of class.

General Unit Schedule (contd): See course website for specific deadlines and due dates. ENDING THE UNIT 1. Take a Performance Assessment (PA) on Sakai. a. Info: Multiple choice quiz on the unit (videos and lecture unit topics). b. Individual submission. c. General due date: the Sunday after the last lecture in the unit is finished (assessments posted on Sakai on Wednesdays). 2. Complete and submit Problem Sets (PS) from the Book on Sakai. a. Individual submission. b. General due date: the Friday after the last lecture in the unit is finished.

General Unit Schedule (contd): See course website for specific deadlines and due dates. ENDING THE UNIT 1. Take a Performance Assessment (PA) on Sakai. a. Info: Multiple choice quiz on the unit (videos and lecture unit topics). b. Individual submission. c. General due date: the Friday after the last lecture in the unit is finished. 2. Complete and submit Problem Sets (PS) from the Book on Sakai. a. Individual submission. b. General due date: the Sunday after the last lecture in the unit is finished.

General Lab Schedule: See course website for specific deadlines and due dates. 10 Labs that Roughly Correspond to Unit Topics 1. Each Thursday begin a new lab with your group in the lab section that you are assigned to. a. Lab TA will help answer questions as you work through the lab with your group. 2. Have a lab group member submit the completed lab on Sakai before the next Thursday lab session.

Grades Participation Attendance (3 late or unexcused absences dropped) Peer evaluation Other Participation In-class Piazza Forum participation Application Exercises 5% Problem sets (drop the lowest grade) 10% Labs (drop the lowest grade) 10% 10% (75% individual, 25% team) Readiness assessments (drop the lowest grade) Performance assessments (drop the lowest grade) 5% Project 10% Midterm 1 12.5% Midterm 2 12.5% Final 25% Grading: Grades may be curved at the end of the semester. Cumulative numerical averages of 90 - 100 are guaranteed at least an A-, 80 - 89 at least a B-, and 70 - 79 at least a C-, however the exact ranges for letter grades will be determined after the final exam. The more evidence there is that the class has mastered the material, the more generous the curve will be.

Check with the Syllabus!

Quiz/Submission Process Recap Sakai: Submit unit Problem Sets (individual) Submit Labs (one person submits from each group) Take Performance Assessments (individual) Project proposal submission (one person submits from each group). Record of emails sent out to the class. Check grades and solutions/answers to graded components. Piazza: piazza.com/duke/fall2018/sta101001f18 Ask content related questions and participate on the forms. Coursera: Get unit learning objectives and watch videos. R-Studio: R Interface for conducting the labs. Course Website: Due dates for everything. Check in regularly!