Lecture 0: Introduction Statistics 101 Thomas Leininger May 15, - PowerPoint PPT Presentation

Lecture 0: Introduction Statistics 101 Thomas Leininger May 15, 2013

Syllabus & policies Logistics General Info Professor: Thomas Leininger - thomas.leininger@duke.edu Old Chemistry 114 Lecture: MTWThF, 12:30–1:45pm, Old Chem 025 Lab: TTh, 2–3pm, Link Classroom 6 Textbook OpenIntro Statistics Diez, Barr, C ¸ etinkaya-Rundel CreateSpace, 2 nd Edition, 2012 Calculator (Optional) You might need a four-function calculator that can do square roots for this class. No limitation on the type of calculator you can use. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 1 / 31

Syllabus & policies Logistics Webpage http://stat.duke.edu/ ∼ tjl13/s101/sta101.html All announcements and assignments will be posted on this website under the schedule tab. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 2 / 31

Syllabus & policies Logistics Grading - Problem sets: 20% - Project: 20% - Labs: 15% - Midterm: 15% - Quizzes: 10% - Final: 20% 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 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 3 / 31

Syllabus & policies Details Course goals & objectives Recognize the importance of data collection, identify limitations 1 in data collection methods, and determine how they affect the scope of inference. Use statistical software to summarize data numerically and 2 visually, and to perform data analysis. Have a conceptual understanding of the unified nature of 3 statistical inference. Apply estimation and testing methods to analyze single variables 4 or the relationship between two variables in order to understand natural phenomena and make data-based decisions. Model numerical response variables using a single explanatory 5 variable or multiple explanatory variables in order to investigate relationships between variables. Interpret results correctly, effectively, and in context without 6 relying on statistical jargon. Critique data-based claims and evaluate data-based decisions. 7 Complete a research project employing statistical inference. 8 Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 4 / 31

Syllabus & policies Details Units and major topics Unit 1 Introduction to data: Observational studies and non-causal inference, principles of experimental design and causal inference, exploratory data analysis: description, summary and visualization, introduction to statistical inference. Unit 2 Probability and distributions: The basics of probability and chance processes, Bayesian perspective in statistical inference, the normal distribution. Unit 3 Framework for inference: Central Limit Theorem and sampling distributions Unit 4 Statistical inference for numerical variables Unit 5 Statistical inference for categorical variables Unit 6 Simple linear regression: Bivariate correlation and causality, introduction to modeling Unit 7 Multiple linear regression: More advanced modeling Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 5 / 31

Syllabus & policies Details Lectures Lecture slides will be posted on the course webpage (under schedule) by noon the day of the course (hopefully). In order to be able to keep up with the pace of the course and not fall behind you must attend the lectures. Introduction of concepts as well as hands-on activities and exercises to complement them. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 6 / 31

Syllabus & policies Details Problem sets Objective: Help you develop a more in-depth understanding of the material and help you prepare for exams and projects. Questions from the textbook. Due at the beginning of class on the due date. Generally assigned on Tuesdays and Fridays. Show all your work to receive credit. Welcomed and encouraged to work with others, but turn in your own work. Lowest score will be dropped. No make-ups. Excused absences do not excuse homework. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 7 / 31

Syllabus & policies Details Labs Objective: Give you hands on experience with data analysis using a statistical software and provide you with tools for the projects. http://beta.rstudio.org Labs will be on Tuesday and Thursday in the Link. You can email me your lab report or turn it in at class. Any labs during the week are due the following Monday by the beginning of class. Add your gmail address to Google doc by 4pm today to create an RStudio account. Can be done in teams of 2–3. Lowest lab score will be dropped. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 8 / 31

Syllabus & policies Details Projects Objective: Give you independent, applied research experience using real data and statistical methods. choose a research question, find data, analyze it, write up your results. statistical inference exploring the distributional characteristics of one variable or relationship between two variables. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 9 / 31

Syllabus & policies Details Quizzes Objective: Help you identify any knowledge gaps and help me pace the class. These will be given on Fridays (except the day of the midterm). You are welcome to use notes or the book. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 10 / 31

Syllabus & policies Details Exams Midterm: Friday, June 7 Final: Wednesday, June 26, 9am-12pm (Cumulative) Exam dates cannot be changed. No make-up exams will be given. If you cannot take the exams on these dates you need to talk to me by the Add/Drop deadline (this Friday). You must bring a calculator to the exams (no cell phones, iPods, etc.) and you are also allowed to bring one sheet of notes (“cheat sheet”). This sheet must be no larger than 8 1 2 ” × 11 ” and must be prepared by you (no photocopies). You may use both sides of the sheet. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 11 / 31

Syllabus & policies Details Work load You are expected to put in 2-3 hours of work outside of class every day. Some of you will do well with less time than this, and some of you will need more. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 12 / 31

Syllabus & policies Support Email & office hours I will regularly send announcements by email, so make sure to check your email daily. While email is the quickest way to reach me outside of class, it is much more efficient to answer most statistical questions in person. I will hold office hours on Mondays and Wednesdays from 2–4pm in Old Chem 114. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 13 / 31

Syllabus & policies Support Other learning resources Aside from my office hours, you can also make use of the Academic Resource Center( http://web.duke.edu/arc ). Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 14 / 31

Syllabus & policies Support 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. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 15 / 31

Syllabus & policies Policies Policies I Late work policy for problem sets and labs reports: after class on due date: lose 10% of points next day: lose 20% of points later than next day: lose all points Late work policy for projects: 10% off for each day late. There will be no make-up for labs, problem sets, projects, or exams. If a quiz or 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. The final exam must be taken at the stated time. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 16 / 31

Syllabus & policies Policies Policies II You must take the final exam and turn in the project in order to pass this course. 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. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 17 / 31

Syllabus & policies Policies 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 Conduct. Additional penalties may also be assessed if deemed appropriate. If you have any questions about whether something is or is not allowed, ask me beforehand. Some examples: Use of disallowed materials (including any form of communication with classmates or looking at a classmate ˜ Os work) during exams. Plagiarism of any kind. Use of outside answer keys or solution manuals for the homework. Statistics 101 (Thomas Leininger) Lecture 0: Introduction May 15, 2013 18 / 31