Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 342383 Statistics in Education 1 3.0 3 ▲ Sheng-Lun Cheng GTVE 21 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	342383	Statistics in Education	1	3.0	3	▲	Sheng-Lun Cheng	GTVE	21	0

Course Plan：

Instructor Sheng-Lun Cheng　Office Hours

Email scheng@ntut.edu.tw

Last Updated 2024-12-13 11:05:38

Course Objective This is a course designed to provide an introduction to the principles and methods of statistics using R. The overall goal of the course is to provide graduate students with a foundation for quantitative data analysis that will allow them to build future skills. This course provides an applied approach to statistical analysis for research. Topics include an introduction to univariate and bivariate distributions, data displays, descriptive statistics, the standard normal distribution, student’s t distribution, inferential statistical methods for testing means, correlation, simple/multiple linear regression, and mediation. This course also introduces the student to the R data analysis system. No formal course prerequisites.

Course Schedule 01: Course Introduction 02: Review: Research Methods [Descriptive] 03: Analyzing Data: Descriptives & Going Beyond 04: The Concept of Statistical Modeling 05: Making Inferences about Populations 06: Testing Research Questions 07: Data Displays & Graphs 08: Statistical Assumptions and Bias 09: Mid-term [Relational] 10: Relations Between Variables (bivariate correlation) 11: Predictions (simple linear regression & multiple linear regression) 12: Simple Mediation Model 13: Parallel Multiple Mediator Model 14: Parallel Multiple Mediator Model with Multiple Causal Antecedents 15: Parallel Multiple Mediator Model with Multiple Causal Antecedents and Consequent Variables [Experimental] 16: Mean Comparisons: t-test (independent and paired) 17: General Linear Model: ANOVA & ANCOVA 18: Final

Evaluation and grading policy Attendance: 20% Mid-term exam: 40% Final: 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Field, A., Miles, J., & Field, Z. (2013). Discovering statistics using R. Sage. Navarro, D. (2019). Learning statistics with R: A tutorial for psychology students and other beginners. (https://learningstatisticswithr.com/book/index.html).

The access to curricular consultation The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.

The course corresponds to the SDGs SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）
SDG10：減少不平等（Reduced Inequalities）

Does the course incorporate AI

Note
Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.

Instructor	Sheng-Lun Cheng　Office Hours
Email	scheng@ntut.edu.tw
Last Updated	2024-12-13 11:05:38
Course Objective	This is a course designed to provide an introduction to the principles and methods of statistics using R. The overall goal of the course is to provide graduate students with a foundation for quantitative data analysis that will allow them to build future skills. This course provides an applied approach to statistical analysis for research. Topics include an introduction to univariate and bivariate distributions, data displays, descriptive statistics, the standard normal distribution, student’s t distribution, inferential statistical methods for testing means, correlation, simple/multiple linear regression, and mediation. This course also introduces the student to the R data analysis system. No formal course prerequisites.
Course Schedule	01: Course Introduction 02: Review: Research Methods [Descriptive] 03: Analyzing Data: Descriptives & Going Beyond 04: The Concept of Statistical Modeling 05: Making Inferences about Populations 06: Testing Research Questions 07: Data Displays & Graphs 08: Statistical Assumptions and Bias 09: Mid-term [Relational] 10: Relations Between Variables (bivariate correlation) 11: Predictions (simple linear regression & multiple linear regression) 12: Simple Mediation Model 13: Parallel Multiple Mediator Model 14: Parallel Multiple Mediator Model with Multiple Causal Antecedents 15: Parallel Multiple Mediator Model with Multiple Causal Antecedents and Consequent Variables [Experimental] 16: Mean Comparisons: t-test (independent and paired) 17: General Linear Model: ANOVA & ANCOVA 18: Final
Evaluation and grading policy	Attendance: 20% Mid-term exam: 40% Final: 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Field, A., Miles, J., & Field, Z. (2013). Discovering statistics using R. Sage. Navarro, D. (2019). Learning statistics with R: A tutorial for psychology students and other beginners. (https://learningstatisticswithr.com/book/index.html).
The access to curricular consultation	The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG10：減少不平等（Reduced Inequalities）
Does the course incorporate AI
Note	Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.