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CORNELL STSCI 2150 - Course Information - Full

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Introductory Statistics for Biology STSCI 2150 Spring 2019, 4 credits This course will provide an introduction to data analysis and statistical inference with an emphasis on practical use of statistics. Statistical applications include the areas of biology, medicine, nutritional science, epidemiology and environmental science. The weekly computer labs will teach graphical analysis and statistical computation using R. Emphasis will be on concepts and the careful modeling of biological data, so that statistical methods are applied properly and sound conclusions are reached. Topics will include populations and sampling, graphical display of data, numerical methods for summarizing and describing data, probability, expectation and variance, estimation, hypothesis testing, the binomial and Poisson distributions, inference about proportions, goodness-of-fit tests and contingency tables, normal distributions and the central limit theorem, inference about the mean and variance of one or two normal populations, correlation and regression. There are no prerequisites and the course is open to all Cornell students. The course is worth four credits: three lectures and one computer lab per week. There will be weekly homework, three prelims, and a final exam. The weekly homework assignments are a mix of statistical analysis with R and problems which involve writing and hand calculations. Textbook: The Analysis of Biological Data by Whitlock and Schluter (both authors are biologists). Either the 1st edition or 2nd edition are fine. This textbook is excellent – past students will vouch for this. Class Time and Location: MWF 2:30 – 3:20 pm, 255 Olin Hall Prerequisites: None Instructor: Melissa Smith, Lecturer, Department of Statistical Science 295 Ives Hall [email protected] Computer labs Course Overlap Held in various classrooms. The first two weeks, they will be held in various computer labs on campus at locations that are different from those listed in Student Center. Be sure to check the announcements in Canvas for locations. Thur 11:40 am to 12:55 pm Thur 2:55 pm to 4:10 pm Fri 8:40 am to 9:55 am Fri 11:40 am to 12:55 pm Forbidden Overlap: A student cannot receive credit for this course and for MATH 1710, SOC 3010, PSYCH 3500, ENGRD 2700, ILRST 2100, PAM 2100, AEM 2100, BTRY 3010, or AP Statistics.Office Hours: TBA, see Canvas. I’m also available other times, by appointment. The TA’s will all hold office hours too. Website: The course website is available through http://canvas.cornell.edu/. We will use Piazza also. Please register with the site and check it regularly. This is a good venue for getting answers to questions that you have about the homework assignments. Statistical Software: We will be using the software package, R. This is a free package and can be downloaded from the Web. (You do not need to do this before the course has begun. We will go over it in the first lab period.) Performance Evaluation: Attendance: Attendance at lectures is expected but not required. You are responsible for being aware of the announcements and content. Lab attendance will be taken at the first six labs. There are a total of 14 labs. Attendance at these labs is especially important in order to learn R. You will receive full credit for lab attendance if you attend at least 5 of the first 6 labs. Pre-Lecture Quizzes (online): Online quizzes are given, in order to help you keep up with the reading in the course. Each quiz has 5 questions, easily answered if you have done the reading. They are based on the textbook material and you get multiple, unlimited tries. They are posted at the end of one class and are due before the start of the next class. There are no quiz make-ups, except for students who added the course late. Homework / Lab assignments: The homework assignments are to complete the exercises located at the end of each week’s lab document. The lab documents are posted on the course website and will generally be due one week after posting. Generally homework is due one week after posting, on Wednesdays at 11:59 pm. This is occasionally shifted due to vacation breaks and prelim scheduling. Students may discuss homework problems with one another, but only at the level of a “corridor conversation” with no notes taken. Homework that is late receives 10% off for within 24 hours late and 20% for within 24 to 48 hours late. A zero after that. If you have a good reason why you cannot meet a deadline (such as sickness or a family situation), please check with me, no later 6 pm on the day of the deadline. In these cases some arrangement can usually be found. Homework submission is to be done electronically through Canvas. It is your responsibility to be sure that your homework was actually turned in. Don’t just think that you turned it in. Check and then double-check that your homework was actually submitted. Being compulsive about this would be good. Save the Turnitin receipt that is produced when you turn in your homework.Exams: There will be three prelims, all of them in the evening. There will be a final exam that is mostly non-cumulative. For exams, you may bring a non-graphing calculator that does not have any communication capability. Do not bring cellphones. Grade Calculation: Your grade will be based on homework (18%), lab attendance (3%), pre-lecture quizzes (3%), in-class exercises (1%), three prelims (17% each), and the final exam (24%). Homework assignments are equally weighted. The lowest score of all the homework assignments will be replaced by the next lowest homework score. If there is a dispute about grading (a homework set or an exam), you may turn in the work with a written request for a regrade within a week of the work being graded. All of the work, and not just the disputed question, will be regraded. Grade scale: A+ = 97-100, A = 93-96, A- = 90-92, B+ = 87-89, B = 83-86, B- = 80-82, C+ = 77-79, C = 73-76, C- = 70-72, D+ = 67-69, D = 63-66, F <= 62 Academic Conduct: Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit should be the student’s own work. Do NOT sign-in your friends during labs. This is a violation of the Code of Academic Integrity. The TA’s will be watching for this. I treat violations of Academic Integrity seriously. Prior violations: 1) Student copied homework solutions on


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