General Info Professor: Dr. Mine C etinkaya-Rundel - - PowerPoint PPT Presentation

Syllabus & policies Logistics General Info Professor: Dr. Mine C ¸ etinkaya-Rundel - mine@stat.duke.edu Old Chemistry 213 Lecture 0: Introduction Teaching Gary Larson - gary.larson@stat.duke.edu Assistants: Yingbo Li - yl118@duke.edu Shaan Qamar - shaan.qamar@duke.edu Statistics 101 Anthony Weishampel - anthony.weishampel@duke.edu Mine C ¸ etinkaya-Rundel Lecture: Tuesdays and Thursdays, 1:25 - 2:40 at Soc Sci 136 January 10, 2013 Lab: Mondays at Old Chem 101 • 08:30am - 09:45am - Anthony • 10:05am - 11:20am - Gary • 11:45am - 01:00pm - Anthony • 01:25pm - 02:40pm - Gary • 03:05pm - 04:20pm - Gary Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 1 / 39 Syllabus & policies Logistics Syllabus & policies Logistics Required materials Clicker registration http://iclicker.com/support/registeryourclicker Textbook OpenIntro Statistics Diez, Barr, C ¸ etinkaya-Rundel CreateSpace, 2 nd Edition, 2012 ISBN: 978-1478217206 Clicker i > clicker2. ISBN: 1429280476, available at the Duke textbook store, i > clicker website, or Ama- zon, used clickers from former students (see Google doc). Calculator (Optional) You might need a four function calcu- lator that can do square roots for this class. No limitation on the type of calculator you can use. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 2 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 3 / 39

Syllabus & policies Logistics Syllabus & policies Logistics Webpage Grading http://stat.duke.edu/courses/Spring13/sta101.001 - Clicker questions: 5% - Project 1: 10% All announcements and assignments will be posted on this website - Project 2: 10% - Problem sets: 7.5% under the schedule tab. - Midterm: 15% - Labs: 7.5% - Final: 25% - Readiness assessments: 15% (2/3 individual, 1/3 team) - Peer evaluations: 5% Grades curved at the end of the course after overall averages have been calculated. Average of 90-100 guaranteed A-. Average of 80-90 guaranteed B-. Average of 70-80 guaranteed C-. The more evidence there is that the class has mastered the material, the more generous the curve will be. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 4 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 5 / 39 Syllabus & policies Details Syllabus & policies Details Course goals & objectives Units and major topics Recognize the importance of data collection, identify limitations 1 Unit 1 Introduction to data: Observational studies and non-causal in data collection methods, and determine how they affect the inference, principles of experimental design and causal scope of inference. inference, exploratory data analysis: description, summary and Use statistical software to summarize data numerically and 2 visualization, introduction to statistical inference. visually, and to perform data analysis. Have a conceptual understanding of the unified nature of 3 Unit 2 Probability and distributions: The basics of probability and statistical inference. chance processes, Bayesian perspective in statistical inference, Apply estimation and testing methods to analyze single variables 4 the normal distribution. or the relationship between two variables in order to understand natural phenomena and make data-based decisions. Unit 3 Framework for inference: Central Limit Theorem and sampling Model numerical response variables using a single explanatory 5 distributions variable or multiple explanatory variables in order to investigate relationships between variables. Unit 4 Statistical inference for numerical variables Interpret results correctly, effectively, and in context without 6 Unit 5 Statistical inference for categorical variables relying on statistical jargon. Unit 6 Simple linear regression: Bivariate correlation and causality, Critique data-based claims and evaluate data-based decisions. 7 Complete two research projects: one that employs simple 8 introduction to modeling statistical inference and another that employs more advanced Unit 7 Multiple linear regression: More advanced modeling modeling techniques. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 6 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 7 / 39

Syllabus & policies Details Syllabus & policies Details Course structure Teams Seven learning units. Assigned to teams of 4-5 students based on data from the Set of learning objectives and required and suggested readings, survey and the pre-test. videos, etc. for each unit. Teams are heterogeneous with respect to stats exposure and Prior to beginning the unit, complete the readings and familiarize homogenous with respect to majors and/or interests - to the yourselves with the learning objectives. extent that it’s possible. Begin a new unit with a readiness assessment: individual, then Once team assignments have been made there is no option for team. changing teams, other than under extraordinary circumstances. Tuesdays and Thursdays: Split rest of the class time between Six peer evaluations throughout the semester as well as other lecture (supplemented with active participation and peer measures to ensure the functionality of the teams and to make instruction via clickers) and team application exercises. sure all team members contribute to the team work. Mondays: Complete lab assignments in teams. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 8 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 9 / 39 Syllabus & policies Details Syllabus & policies Details Lectures Clicker questions Objective: Make you an active participant and help me pace the class. On new material introduced in class that day. Lecture slides will be posted on the course webpage (under Credit for clicking in, regardless of whether you have the correct schedule) by noon the day of the course. answer (must answer at least 75% of the questions that day). In order to be able to keep up with the pace of the course and Up to two unexcused late arrivals or absences will not affect your not fall behind you must attend the lectures. clicker grade. Introduction of concepts as well as hands on activities and If one person is simultaneously using two or more clickers, all exercises to complement them. students involved will receive a 0 for an overall clicker grade. Grading will start on January 22. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 10 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 11 / 39

Syllabus & policies Details Syllabus & policies Details Problem sets Labs Objective: Help you develop a more in-depth understanding of the Objective: Give you hands on experience with data analysis using a material and help you prepare for exams and projects. statistical software and provide you with tools for the projects. Questions from the textbook. http://beta.rstudio.org Due at the beginning of class on the due date. Show all your work to receive credit. Add your gmail address to Google doc by 5pm today to create an Welcomed and encouraged to work with others, but turn in your RStudio account. own work. Complete in teams. Lowest score will be dropped. Lowest lab score will be dropped. No make-ups. If you do not attend a lab section, you are not eligible for credit Excused absences do not excuse homework. on that lab. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 12 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 13 / 39 Syllabus & policies Details Syllabus & policies Details Projects Readiness assessments Objective: Encourage you to complete the reading assignment prior to coming to class and evaluate your conceptual understanding of the learning objectives. Objective: Give you independent applied research experience using real data and statistical methods. 10 multiple choice questions, at the beginning of a unit. Project 1: Conceptual questions addressing the learning objectives of the individual new unit, assessing familiarity and reasoning, not mastery. statistical inference exploring the distributional characteristics of Take the individual readiness assessment using your clickers, one variable or relationship between two variables and then re-take the same assessment in teams. choose a research question, find data, analyze it, write up your Your performance on both assessments factors into your final results grade: score for each assessment is a weighted average of the Project 2: in teams, presentation, multiple linear regression, individual (2/3) and team (1/3) scores. more info later First readiness assessment next Tuesday, for practice, not graded. 6 graded readiness assessments, lowest score will be dropped. Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 14 / 39 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 15 / 39