Hiroshima University Syllabus

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Academic Year School/Graduate School Lecture Code 2024Year School of Informatics and Data Science KA123001 Specialized Education カテゴリカル・データ分析（CDA） カテゴリカル・データブンセキ（シーディーエー） Basic and practice in Categorical data analysis MONDEN REI モンデン　レイ Higashi-Hiroshima 2nd-Year,  Second Semester,  4Term (4T) Thur1-4：EDU K201 Lecture 2.0 B : Japanese／English 2 : Undergraduate Low-Intermediate 25 : Science and Technology 02 : Information Science Second year Bachelor 4th semester Categorical Data Analysis, Generalized Linear Model, R program Learn statistical knowledge and be able to apply them Computer Science Program（Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.Data Science Program（Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.Intelligence Science Program（Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. To understand categorical data analysis methods and its application in R program lesson1: Guidance, Rmarkdownlesson2: Review lesson3: Analyzing contingency tables (theory)lesson4: Analyzing contingency tables (practical)lesson5: Generalized Linear Models (theory)lesson6: Generalized Linear Models (practical)lesson7: Logistic regression (theory)lesson8: Logistic regression (practical)lesson9: Multiple logistic regressionlesson10: Building and applying logistic regression models (theory)lesson11: Building and applying logistic regression models (practical)lesson12: Multicategory logit modelslesson13: Models for matched pairslesson14: Generalized Linear Mixed models lesson15: Loglinear models for contingency tables and countsAn assignment will be given for each lecture. By handing in the assignments, your attendance will be counted. Reference: (I will explain about this on the 1st lecture about this. No need to buy)An Introduction to Categorical Data Analysis 3rd ed. Alan Agresti PC(each student is expected to bring their own PC to program during the course) Reading the reference can be helpful to prepare and review the lectures The lecture follows a hybrid style (i.e., face-to-face and online). Final report (55%), Assignments (40%), Attitude toward the class (5%). Please fill in the class improvement questionnaire which is carried out on all classes.Instructors will reflect on your feedback and utilize the information for improving their teaching.
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