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U of M PUBH 6450 - Biostatistics I

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© 2018 Regents of the University of Minnesota. All rights reserved. The University of Minnesota is an equal opportunity educator and employer. Printed on recycled and recyclable paper with at least 10 percent postconsumer waste material. This publication/material is available in alternative formats upon request to 612-624-6669. PUBH 6450, SECTIONS 001-007 Biostatistics I Fall 2020 COURSE & CONTACT INFORMATION Credits: 4 Meeting Day(s): Tuesday and Thursday Meeting Time: 1:25 p.m. – 3:20 p.m. Meeting Place: Health Sciences Education Center 3-110 or online Instructor: Marta Shore Email: [email protected] Q and A: Wednesdays TA: Olivia Zang Email: [email protected] Lab: 002 Tuesday 3:35 – 4:25 p.m. Q and A: Thursdays TA: Robert Aidoo Email: [email protected] Lab: 003 Tuesday 5:45 – 6:35 p.m. Q and A: Fridays TA: Linnea York Email: [email protected] Lab: 004 Wednesday 9:05 a.m. – 9:55 a.m. Q and A: Tuesdays TA: Mingming Pan Email: [email protected] Lab: 005 Wednesday 12:20 p.m. – 1:10 p.m. Q and A: Saturdays TA: Jessica Hellner Email: [email protected] Lab: 006 Thursday 10:10 a.m. – 11:00 a.m. Q and A: Mondays TA: David Schneck Email: [email protected] Lab: 003 Thursday 12:20 p.m. – 1:10 p.m. Q and A: Sundays All labs take place via Zoom All TA office hours take place in Q and A forums on the Canvas site2 Office Hours (Weekly Q and A session unless otherwise noted) Monday Jessica Tuesday Linnea Wednesday Marta Thursday Olivia Friday Robert Saturday Mingming Sunday David COURSE DESCRIPTION In this course, we will explore the basic concepts of exploratory data analysis and statistical inference, including: descriptive statistics, random variables and their distributions, point/interval estimation for means, proportions, and odds/risk, hypothesis testing, ANOVA, simple regression/correlation, multiple regression, and nonparametric methods (if possible). We will focus on health science applications using output from statistical packages. COURSE PREREQUISITES College Algebra (e.g. Math 1031), health science grad student, or instructor permission. COURSE GOALS & OBJECTIVES By the end of the course, students should have a basic understanding of the fundamentals of biostatistical methods. This includes: ● Summarizing data with numerical measures and graphs ● Basic concepts of randomness and data distributions ● Point/Interval estimation for categorical and continuous outcomes ● Hypothesis testing for categorical and continuous outcomes ● Simple and multiple linear regression ● Basic SAS and/or R programming language skills METHODS OF INSTRUCTION AND WORK EXPECTATIONS Course Workload Expectations PubH 6450: Biostatistics I is a 4-credit course. The University expects that for each credit, you will spend a minimum of three hours per week attending class, reading, studying, and completing assignments, etc. over the course of a 15-week term. Thus, this course requires approximately 180 hours of effort spread over the course of the term in order to earn an average grade. Methods of Instruction The course will utilize both traditional lectures and active learning experiences. Here is the breakdown of the weekly work expectations: ● Beginning of the week: Students will be expected to prepare for each class meeting by reading from the textbook and working through the lab documents. ● In-class on Day 1 of the week: The first part of class will be devoted to working collaboratively in a small group on the quiz that was completed at the end of the previous week (see more information below). Because of this, it will be essential that you attend class on Day 1 of the week so that you can contribute to your group’s learning. Groups will be required to submit their answers via Canvas. The remaining time will be devoted to learning the topic of the week via a lecture. ● Between Day 1 and Day 2: Students are expected to look over the lectures and readings for the week and prepare any questions they have. In addition, students are expected to attend lab with any questions or issues regarding the lab handouts and to review the answers. After students attend lab, they are encouraged to download the Problem Set and review the questions in preparation for day 2 of class.3 ● In-class on Day 2 of the week: The primary goal of class will be to work on a Problem Set that allows you to apply and further solidify your knowledge of the concept and of analyzing the data via your chosen software while having instructors available for assistance. Class may also include any unfinished lectures and any additional clarifications or information. ● After class on day 2: students will be working collaboratively (with guidance from instructors and TAs) to create the answer key for the Problem Set. Your learning experience will be thus dependent–to some extent–on your classmates and vice versa. Each student will be expected to contribute at least once to the key each week. Your contribution to the collaborative key will be due each Saturday by 5:00pm. ● At the end of the week: An online quiz covering the material of the week, as well as concepts from earlier weeks, will be due each Sunday by 11:59pm. Students will be expected to complete this end-of-week quiz independently. ● At the end of the week (approximately every other week): Students will be expected to complete a one-page analysis report independently in statistical software. Your report will be due Sunday by 11:59pm. ● The first 20 minutes of the first class the following week: Students will sit with their randomly assigned groups of 4 to retake the quiz collaboratively. The questions will all be multiple choice, matching, or true/false. At the end of the 20 minutes, the instructor will look at the results of the quiz and go over any questions that were missed by several groups. Lab In the weekly lab sessions, you will have a chance to ask for help with your chosen software (SAS or R). Your lab TA and your fellow students will help you work through any issues you had with the software. In addition, your TA will review the Guided Questions that ask you to interpret the output from the code in the document, and the Challenge questions that try to push you in your coding abilities and may force you to utilize external resources (e.g., internet) to answer the question. Reports There will


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