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Lecturer: Hsin-I Hsiao (蕭心怡)
Email: hi.hsiao@ntou.edu.tw
Phone: (02)2462-2192＃5139
Webpage: https://fs.ntou.edu.tw/p/412-1073-8839.php?Lang=zh-tw
Course ID: M3201J4B
Credits: 2
Objective:
The teaching purpose of this course is to let students understand how to use statistical software to solve biostatistical problems.
Course Prerequisites: None.
Outline: This course introduces the SPSS and open source R software language that can be implemented in biostatistics for data organization, statistical analysis, and graphical presentation. Beginning with a focus on data from a parametric perspective, the teacher will address topics such as Student t-Tests for independent samples and matched pairs; one-way and two-way analyses of variance; and correlation and linear regression.
Teaching Method: The teaching materials used in this course are created by the teacher. The teacher will introduce and demonstrate statistics software to students and each week students should finish the homework. In the first lesson, the teacher will explain the background, so that students can have a clear idea of the content of the course. By the end of course, there will be a final examination.
Reference:
Belmont, 2006, Fundamentals of biostatistics, Thomson-Brooks. Cleophas T. J., Zqinderman, A. H. 2012. SPSS for stsaters.Springer https://doi.org/10.1007/978-94-007-4804-0
MacFarland, T. W., Yates, J. M. 2021. Using R for biostatis-tics. Springer International Publishing, https://doi.org/10.1007/978-3-030-62404-0
Course Schedule (subject to change):
Week Schedule
1 Ch1 Introduction-what is biostatistics?
2 Ch 2 Descriptive statistics: organization and display of data
3 Ch3 Descriptive statistics: mean, measures of central tendency and measures of dispersion
4 Ch4 Probability, Normal distribution
5 Ch 5 Confidence interval
6 Ch 6 Calculate a test statistic
7 Ch 7 Two sample test
8 Wrap up!
9 Middle exam
10 Ch 8 Experiment planning
11 Ch 9 ANOVA
12 Ch 10 MANOVA
13 Ch 11 Correlation
14 Ch 12 Linear regression
15 Ch 13 Multiple regression
16 Ch 14 Chi-square tests
17 Wrap up!
18 Final exam week
1. Introduction
2. Table
3. Figure
4. Summary statistics
5. Summary statistics
6. t-test I
7. t-test II
8. t-test III
9. ANOVA I
10. ANOVA II
11. MANOVA
12. MANOVA
13. Correlation
14. Chi-square test
15. Linear regression
16. Examination
Evaluation:
Mid-term exam 30%,
final exam 30%,
Attendance 10%,
Assignment 30
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