STAC51编程写作、辅导Data Analysis

” STAC51编程写作、辅导Data AnalysisDepartment of Computer and Mathematical SciencesSTAC51: Categorical Data AnalysisWinter 2021Instructor: Sohee KangE-mail: sohee.kang@utoronto.caOffice: IC 483Online Office Hours: Monday 5-6 pm and Wednesday 5-6 pm(416) 208-4749TA: Bo Chen TA: Lehang ZhongE-mail: bojacob.chen@mail.utoronto.ca E-mail: lehang.zhong@mail.utoronto.caCourse Description: In this course we discuss statistical models for categorical data. Contingencytables, generalized linear models, logistic regression, multinomial responses, logit models fornominal responses, log-linear models for two-way tables, three-way tables and higher dimensions,models for matched pairs, Repeated categorical response data, correlated and clustered responsesand statistical analyses using R. The students will be expected to interpret R codes and outputson tests and the exam.Prerequisite(s): STAB27H3 or STAB57H3 or MGEB12H3 or PSYC08H3Credit Hours: 3Required Text: An Introduction to Categorical Data Analysis, 3rd EditionAuthor(s): Alan AgrestiWebLink for 2nd edition: httpss://search.library.utoronto.ca/details?7961944Sub-text1: Categorical Data with R, 3rd editionAuthor: Alan AgrestiSub-text2: Analysis of Categorical Data with R (2014)Author:Bilder C. and Loughin T.Course Objectives:At the completion of this course, students will be able to:1. use R software to conduct categorical data analysis.2. identify designs of contingency tables and recommend appropriate measures of associationand statistical tests.3. develop models for binary response and polytomous categorical responses, interpret resultsand diagnose model fits.4. interpret and communicate categorical data methods to a technical audience.1Grade Components:Case Study and Presentation 15%Assignments 15%Quizzes 15%Midterm Exam 20%Final Exam 30%Attendance 5 %Course Policy: Communication Important announcements, lecture notes, additional material, and other course info willbe posted on Quercus. Check it regularly. You are responsible for keeping up withannouncements from instructors on Quercus and via e-mail. Check Piazza before you Send an e-mail, make sure that you are not asking forinformation that is already on Piazza. In general, I will not answer questions aboutthe course material by e-mail. Such questions are more appropriately discussed duringoffice hours of me or TAs. E-mail is appropriate for private communication. Use your utoronto.ca account andinclude STAC51 in the subject line. Oral AssessmentIf the instructor has a suspicion on your assessment result (the deviance is great) then shewill conduct an oral assessment after. If the oral assessment result confirms the suspicionthen the previous assessment score will be replaced to 0. No makeup quizzes or exams will be given.Learning Components: TutorialStudents are expected to attend the weekly tutorial to gain practical R programming experience.Quizzes will be conducted in tutorial. You need to turn on videos so that TAs caninvigilate. AssignmentsThree assignments (each 5%) will be distributed. All assignments are group works (two teammembers) unless you prefer individual work. QuizThree quizzes (each 5%) will take place after the assignments handed in. Case Study and PresentationStudents will be required to work on a case study as a group and to submit a report. Thesize of the group is maximum of FOUR. You can choose your group members. For a report,students will write R codes and interpret R outputs and will use R Markdown (R package).More details, such as the content and deadline, will be communicated later. No late reportwill be accepted. Each group will present the case study (5 minutes) at the last day oflecture.2 Attendance Attendance is expected and will be taken each class and tutorial. Computing Statistical computing is a key part of the class. In-class analysis will be conductedin R and all course Material (code and data) is in R format. R is free and available fordownload at https://www.r-project.org, and you can find manuals and installation guidelineson this site.For basics in R, here are suggested documents: R for beginners by Emanuel Paradis, AnIntroduction to R by W. N. Venables, D. M. Smith, and the R Core Team, A (very) shortintroduction to R by Paul Torfs and Claudia Brauer. More information and documentationare available on The R Project website. Students are expected to write R codes and interpretR outputs on assignments, tests, and the exam.Outline of Topics:Chapter ContentCh. 1 Introduction Distributions for categorical data Statistical inference for categorical dataCh. 2 Describing contingency tables, independence of categorical variables Comparing proportions, Relative risk, Odds ratioCh. 2 Inference for contingency tables, Chi-squared tests of independence Exact tests for Small samplesCh. 3 Introduction to Generalized Linear Models: Generalized linear models for binarydata, Poisson log linear models, Negative binomial GLMsCh. 4 Logistic RegressionCh. 5 Building, Checking, and applying logistic regression models.Ch. 6 Models for multinomial responses.Ch. 7 Loglinear models for two-way tables, Loglinear models for three-way tables,Inference for loglinear models.Ch 8 Models for matched pairs.3University Policies Academic Integrity:Academic integrity is essential to the pursuit of learning and scholarship in a university,and to ensuring that a degree from the University of Toronto is a strong signal of each studentsindividual academic achievement. As a result, the University treats cases of cheatingand plagiarism very seriously. The University of Torontos Code of Behaviour on AcademicMatters ( https://www.governingcouncil.utoronto.ca/policies/behaveac.htm) outlines the behavioursthat constitute academic dishonesty and the processes for addressing academic offences.Potential offences include, but are not limited to:In papers and assignments: Using someone elses ideas or words without appropriate acknowledgment. Submitting your own work in more than one course without the permission of the instructor. Making up sources or facts. Obtaining or providing unauthorized assistance on any assignment.On tests and exams: Using or possessing unauthorized aids. Looking at Someone elses answers during an exam or test. Misrepresenting your identity.In academic work: Falsifying institutional documents or grades. Falsifying or altering any documentation required by the University, including (but notlimited to) doctors notes.All suspected cases of academic dishonesty will be investigated following procedures outlinedin the Code of Behaviour on Academic Matters. If you have questions or concerns about whatconstitutes appropriate Academic behaviour or Appropriate research and citation methods, youare expected to seek out additional information on academic integrity from your instructoror from other institutional resources (see https://www.utoronto.ca/academicintegrity/). Accessibility:Students with diverse learning styles and needs are welcome in this course. In particular,if you have a disability/health Consideration that may require accommodations, please feelfree to approach me and/or the AccessAbility Services Office as soon as possible. I willwork with you and AccessAbility Services to ensure you can achieve your learning goals inthis course. Enquiries are confidential. The UTSC AccessAbility Services staff (located inS302) are available by appointment to Assess Specific needs, provide referrals and arrangeappropriate accommodations (416) 287-7560 or ability@utsc.utoronto.ca.如有需要，请加QQ：99515681 或WX：codehelp

“