All collaboratorsshould be appropriately cited in your write up and any detailed R code should also beprovided. Homework assignments are meant to achieve three results:(1) provide practice with the statistical concepts discussed in class, (2) provide practicewith the computational and statistical programing language R, and (3) provide a chanceto demonstrate your mastery of material and highlight areas where more work is needed.You may work in a group, but all write-ups must be done independently. Homework assignments will normally be administered on a bi-weekly basis and will be dueon every other Wednesday. You are expected to read over the class noteseach week and make sure you are familiar with the material as the course progresses.Questions are encouraged. All readings are assumedto be completed before each lecture/seminar. Weekly readings assignments can be found on the course syllabus. Computer labs are available on campus, please consult withthe TA if you have trouble finding the various locations of campus computer labs.
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It is not required that students bring their computers/laptops to lecture and lab (if one isowned), but it is highly recommended since both lecture and lab will make extensive useof the computer software R. It is assumed that the student has taken Math 1272 or 1372 or 1572 (i.e., differentialand integral calculus), CSCI 1113 (introduction C/C++ Programming for Scientists andEngineers) and Stat 3032 (Regression and Correlated Data). At the end of this coursestudents should have a broad grasp of R and its capabilities, and a basic understanding ofkey computational methods in statistics such as optimization, numerical analysis, MCMCtechniques, and simulation methods. Students will cover classicmethods of the bootstrap through modern Bayesian inference. Themain focus of the class will be on statistical programming, graphics, elementary MonteĬarlo, simulation studies, and elementary optimization in R. It isassumed students have basic exposure to computer programming, statistics up throughlinear models and basic knowledge of calculus (i.e., differential and integral calculus). This course is an introduction to statistical computing and will be taught in R.
Name: Mingxuan HanOffice: 495 Ford HallOffice Hours: TU 1:00-3)) PMEmail: Objectives AlmquistOffice: 1168 Social Science BuildingOffice Hours: M 11:00AM - 1:00PMEmail: 61 (not recommended)
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URL: : Requires UMN login and registration in class to access. Lecture: M/W 01:25PM - 2:15PM Mechanical Engineering 18Lab F 12:20PM - 1:10PM Ford Hall 110Lab F 01:25PM - 2:15PM Bruininks Hall 530A
Introduction to Statistical Computing (Statistics 3701)
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