• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Explain fundamentals probability and statistics

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications

   Method of assessment

   1. Written exam
2. Apply hypothesis testing on life sciences data

   Contribution to Program Outcomes

   1. Work effectively in multi-disciplinary research teams

   Method of assessment

   1. Written exam
3. Use biostatistics tools essential to bioengineering

   Contribution to Program Outcomes

   1. Conduct and develop bioengineering applications for relevant sectors such as health and agricultural industry.

   Method of assessment

   1. Written exam
4. Define biased uses of biostatistics in the literature

   Contribution to Program Outcomes

   1. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam

Contents
Week 1:	Descriptive Statistics: Presenting the data from patients
Week 2:	Introduction to Probability: Understanding risks of disease
Week 3:	Introduction to Probability: Conditional probability Classwork1
Week 4:	Quiz -1 Discrete Probability Distributions - binomial, Poisson and negative binomial distributions
Week 5:	Discrete Probability Distributions - hypergeometric distribution and other distributions Classwork2
Week 6:	Quiz -2 Continuous Probability Distributions: Design of clinical trials Classwork3
Week 7:	Estimation Methods - Continuous distributions Midterm Exam 1
Week 8:	Estimation Methods - Discrete distributions Classwork4
Week 9:	Quiz -3 Hypothesis Testing: One-Sample Inference - z-test, t - test, identifying the Differentially Transcribed Genes
Week 10:	Hypothesis Testing: One-Sample Inference - chi-square test, tests on discrete variables Classwork5
Week 11:	Hypothesis Testing: Two-Sample Inference - t-tests Midterm Exam 2
Week 12:	Hypothesis Testing: Two-Sample Inference - chi-square and F tests Classwork6
Week 13:	Quiz -4 Nonparametric Methods: Analysis of Omics Data With Unknown Distribution Classwork7
Week 14:	Biased use of biostatistics in literature - How not to use p-value
Week 15*:	-
Week 16*:	Final Exam
Textbooks and materials:	B. Rosner, "Fundamentals of Biostatistics", Brooks/Cole, Boston, 2010
Recommended readings:	RE. Walpole, RH. Myers, SL. Myers, K. Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, Boston, 2012.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	6, 11	40
Other in-term studies:	-	0
Project:	-	0
Homework:	-	0
Quiz:	4,6,9,13	20
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	15
Own studies outside class:	3	16
Practice, Recitation:	0	0
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	1	4
Own study for mid-term exam:	5	2
Mid-term:	2	2
Personal studies for final exam:	5	1
Final exam:	3	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)