Course Outline

National Taiwan Normal University Course Outline
Fall , 2025

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I.Course information

Serial No.	2702	Course Level	Undergraduate / Master
Course Code	MAC9026	Chinese Course Name	迴歸分析（IB）
Course Name	Regression Analysis (IB)
Department	Department of Mathematics
Two/one semester	1	Req. / Sel.	Sel.
Credits	3.0	Lecturing hours	Lecture hours: 3
Prerequisite Course
Comment
Course Description
Day & Class Period/Location	Thur. 6-8 Gongguan M211
Curriculum Goals		Corresponding to the Departmental Core Goal
1.　Demonstrating a general understanding of key concepts and methodologies in regression analysis		College: 　1-1 Equipped with professional mathematics competences 　1-2 Being able to reason and induct with mathematical logic 　1-4 Possessing the abilities to propose and solve questions in advanced mathematics Master: 　1-1 Equipped with professional mathematics competences 　1-2 Being able to reason and induct with mathematical logic 　1-4 Possessing the abilities to propose and solve questions in advanced mathematics
2.　Demonstrating mastery of knowledge and research skills in problem-solving and application by means of collecting, organizing, interpreting and presenting data		College: 　1-5 Being able to use mathematics as tools to learn other subjects 　1-6 Possessing the capacities to view elementary mathematics from an advanced viewpoint 　2-1 Being able to communicate and express mathematically 　2-2 Possessing the competences of transferring and contextualizing theories in mathematics and mathematics education 　2-3 Being able to lead or collaboratively work with peers 　3-1 Being able to seek out answers with the attitudes of patience, diligence, concentration, and curiosity 　3-2 Possessing the abilities to think independently, criticize, and reflect 　3-3 Being willing to work collaboratively 　3-4 Having insights, intuitions, and senses of mathematics 　4-2 Possessing a consistent and firm attitude toward pursuing truths 　4-3 Possessing a variety of beliefs regarding mathematics values and mathematics learning Master: 　1-5 Being able to use mathematics as tools to learn other subjects 　1-6 Possessing the capacities to view elementary mathematics from an advanced viewpoint 　2-1 Being able to communicate and express mathematically 　2-2 Possessing the competences of transferring and contextualizing theories in mathematics and mathematics education 　2-3 Being able to lead or collaboratively work with peers 　3-1 Being able to seek out answers with the attitudes of patience, diligence, concentration, and curiosity 　3-2 Possessing the abilities to think independently, criticize, and reflect 　3-3 Being willing to work collaboratively 　3-4 Having insights, intuitions, and senses of mathematics 　4-2 Possessing a consistent and firm attitude toward pursuing truths 　4-3 Possessing a variety of beliefs regarding mathematics values and mathematics learning
3.　Demonstrating mastery of computational skills needed to conduct research or inquiry for practice		College: 　2-4 Possessing the competences of lifelong learning 　3-1 Being able to seek out answers with the attitudes of patience, diligence, concentration, and curiosity 　3-2 Possessing the abilities to think independently, criticize, and reflect 　3-4 Having insights, intuitions, and senses of mathematics 　3-5 Having good taste for mathematics Master: 　2-4 Possessing the competences of lifelong learning 　3-1 Being able to seek out answers with the attitudes of patience, diligence, concentration, and curiosity 　3-2 Possessing the abilities to think independently, criticize, and reflect 　3-4 Having insights, intuitions, and senses of mathematics 　3-5 Having good taste for mathematics

II. General Syllabus

Instructor(s)

LU,Tsui-Shan/ 呂翠珊

Schedule

Session 1	Topic: Simple Linear Regression l Introduction l Connection with other subjects and past learning Local and global issues of regression analysis Activities: l Problems that teacher candidates might encounter upon software installation l Linking past knowledge with regression
Session 2	Topic: Multiple Linear Regression l Understanding difference between simple and multiple linear regressions Activity: l Examples of multiple regression discussed among teacher candidates
Session 3	Topic: Inferences in Regression l Implications of theoretical thinking l Approaches to regression analysis l Strategies for derivation of parameters in the model Activity: l Enhancing teacher candidates’ skills of problem-solving and critical thinking
Session 4	Topic: Regression Models for Quantitative and Qualitative Predictors l Strategies to build a model l Construction of model selection and validation Activity: l Discussion on some cases encountered in daily life
Session 5	Topic: Diagnostic l Further understanding of model building l Is there any outlier? l Should we remove the outlier? What if it is informative? Activities: l Tryout and observe the difference l Refining the option of removing concerned observations
Session 6	Topic: Variable Selection l How to select variables for a model? l Are they informative? l Related issues in the real cases Activities: l Tryout of selecting variables by software l Classroom stimulus discussions
Session 7	Topic: Design of Experimental and Observational Studies l Issues and challenges of these frequently-used study designs Activity: l Analysis of several studies and classroom discussions
Session 8	Topic: Assessment Activity: In-class External Assessment
Session 9	Topic: Single Factor Studies l What is factor? l Are the tools for diagnostics learned before suitable? Activity: l Apply analysis of variance to problem-solving
Session 10	Topic: Multi-factor Studies l Understanding of factorial designs l Is it more realistic to consider various factors? Activity: l Readings for the meaning of software output from different factorial studies
Session 11	Topic: Analysis of Covariance l Complex factorial designs l Advising on the design construction Activity: l Strategies employed for real cases
Session 12	Topic: Logistic Regression l Only two possible outcomes l Is the previous approach to derivation suitable? Activity: l Observe wide application of such regression analysis
Session 13	Topic: Poisson Regression l Introduction to count data l How to fit and evaluate such model? l How is it different from fitting logistic regression models? Activity: l Tryout of fitting different regression models for same data l Observing the output and interpreting
Session 14	Topic: Generalized Linear Models l Flexible generalization of ordinary linear regression l Why is it popular? Activity: l Enhancing creative thinking skill through talking with other teacher candidates
Session 15	Topic: Random Effects Models l What is hierarchy? l Controlling for unobserved heterogeneity Activity: l Discussion on examples
Session 16	Topic: Mixed Effects Models l Introduction to repeated measures l Containing fixed and random effects l Why is it useful in all kinds of disciplines? Activity: l Connecting all the regression models
Session 17	Topic: Assessment Activity l In-class External Assessment
Session 18	Topic: Statistician’s Talk l Encouraging teacher candidate’s contribution to effective collaboration and teamwork l Observation of organizing learning materials Activity l Group presentation and interactive discussions l Turn in individual reports

Instructional Approach

Methods

Notes

Formal lecture

Group discussion

Cooperative learning

Case studies

Grading assessment

Methods

Percentage

Notes

Assignments

50 %

Presentation

50 %

Required and Recommended Texts/Readings with References

Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied Linear Regression Models. McGraw Hill, 2004, 4th edition.

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