Jordan University of Science and Technology
Faculty of Engineering
Biomedical Engineering Department
BME 302: Statistics for Biomedical Engineers
Course Catalog
Basic concepts of probability; conditional probability, statistical independence, total probability and Baye’s
Thm.; Random variables ; introduction, discrete and continuous, probability mass & density functions,
cumulative distribution function, moments; Common discrete and continuous distributions; Functions of
random variables; Descriptive Statistics: Describing and summarizing data sets, Histogram, Statistical
distributions; Inferential statistics: hypothesis testing, significance levels, t-test; Analysis of variances
(Anova) and Linear regression.
Text Book(s)
Probability and statistics for engineers and the scientists
Title
Author(s)
Publisher
Year
Edition
Books
Jay L. Devore
Sixth Edition
2003
6th edition
References
Dainel,w.(1999);Biostastics, 7 ed.,John Wiley & Sons, INC.N.Y
th
Montgomry, D.C. and Runger,G.C, Applied statistics and Probability for Engineers”,
second edition, 2003, John Wiely & Sons Inc.
Walpole, R.E and Myers, R.H (1993), Probability and Statistics for Engineers and
Scientists ,5th ed., Macmillan , N.Y
Journals
Internet
links
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Annals of Biomedical Engineering
Journal of Medical Engineering and Technology
Computer Programs and Methods in Medicine
Medical Engineering and Physics
IEEE EMBS Book Series
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Information Technology in Biomedicine
Physiological Measurement
http://www.bmes.org/
http://arjournals.annualreviews.org/loi/bioeng?cookieSet=1
http://www.aami.org/publications/BIT/index.html
http://www.biophysj.org/
http://emb-magazine.bme.uconn.edu/
http://emb-magazine.bme.uconn.edu/
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http://www.iee.org/Publish/Journals/ProfJourn/MBEC/
http://spie.org/app/Publications/index.cfm?fuseaction=journals&type=jbo
http://www.biomedical-engineering-online.com/start.asp
Prerequisites
Prerequisites by topic
Prerequisites by course
Co-requisites by course
Prerequisite for
--------BME 321A
BME 452, BME 562
Objectives
Objectives and Outcomes
Outcomes
Appreciate the role of Biostatistics in
biomedical engineering
(a, b, f, g, h, j, m)
Acquaint basic biostatistics concepts
essential to the understanding of
biomedical engineering and to provide
exposure to a wide range of biomedical
engineering technology in hospitals.
(a, b, f, g, h, j, m)
To see different ways for data
presentations.
(a, b, e, l)
Recognize purpose of using statistics: to control and make decisions.
To provide students with practical
experience of probability and probability
relationships.
(a, b, e, k)
To teach students use statistics in clinical
engineering
(a, b, e, g, h, k)
Understand the basic concepts of probability theory
Given the statistical data and events relationships to show how to investigate
concepts of inferential statistics.
To teach students the concepts of discrete
and continuous random variables.
(a, e)
Analyze problems involving usage of
discrete random variables in binomial
distribution of probability.
(a, e)
Investigate the role random variables in probability distributions.
To analyze problems involving usage of
continuous random variables in Normal
distribution of probability.
(a, e)
The role and usage of normal probability distribution and its role in equipment
maintenance and error theory.
Understand probability distributions: exponential and weigbull.
To teach students aspects of t-test and chitest.
(a, b, c, h)
Use the sample to draw conclusions about the population
Recognize the safety measures taken during the experiment design
The concept and idea of point and interval estimates.
The student will be able to assess a
situation involving data analysis, state the
nature of the question and the null and
alternative hypotheses proposed
(a, b, c, h)
State hypothesis about the population
Decision based on the data analysis involving hypothesis testing.
Define the factors affecting biostatistics assessments.
Learn the basic terminology in biostatistics: sample, population, random
variable, process,…etc.
Identify the basics roles for graphical and numerical presentation in descriptive
statistics..
Recognize the role of self- teaching
Given the design specification students are capable to plan the entire
management process using biostatistics.
The role and usage of discrete probability distribution and its role in equipment
maintenance.
Recognize and use the different types of discrete random variable distribution
functions (Binomial, Poisson, geometric and negative binomial).
2
Objectives and Outcomes
Outcomes
Objectives
Understand the concepts and ideas about
linear regression.
(a, e)
Develop experience with using computers
to analyze experimental data. (a, b, I, m)
Week
1-2
Learn the linear regression model parameters
Estimating the model parameters
Use Minitab / SPSS to perform homework assignments.
Topics Covered
Chapters in Text
Topics
1.1-1.4
Overview and descriptive statistics
Introduction, Pictorial and Tabular
descriptive statistics, Measures of
location and variability
2-3
Probability
4-5
Discrete
random
probability
Counting
techniques,
conditional
probability, and independence.
variables
and
2.3-2.5
3.1-3.6
Discrete random variables and their
probability
distributions,
expected
values, selected discrete probability
distribution functions
5-6
Continuous
probability
random variables
distribution
and
4.1-4.4
Continuous random variables and their
distribution, cumulative distribution
functions,
normal
distribution,
exponential distribution and probability
plots
7-8
Joint Probability
random samples
distributions
and
5.1-5.4
Joints distributed random variables,
Expected values, covariance, and
correlation, the distribution of the
sample mean and the distribution of
linear combination.
8
Point Estimation
6.1-6.2
9-10
Statistical Intervals based on a single
sample
7.1-7.3
The general concepts and methods of
point estimation
Confidence
intervals
and
their
properties, large sample confidence
intervals, intervals based on a normal
distribution
3
11-12
Test for hypothesis based on a s single
sample
8.1,8.2,8.4
Hypothesis and test procedures, test
about population mean and p-values.
13-14
Interference based on two samples
9.1,9.2
Z-test and CI for a difference between
two means, the two sample t-test,
analysis of paired data.
15-16
The simple
correlation
linear
regression
and
12.1-12.3
The simple linear regression model,
estimation of model parameters,
inference about slope parameter and
correlation.
Evaluation
Assessment
Tool
Homework and
semester works
First Exam
Second Exam
Final Exam
Expected Due Date
Weight
One week after homework problems are assigned
10%
According to the University final examination schedule
25 %
25 %
40 %
Teaching & Learning Methods
-
-
Active learning, where students should be active and involved in the learning process inside the classroom, will be
emphasized in the delivery of this course.
Different active learning methods/approaches such as: Engaged Learning, Project-Based Learning, Cooperative
Learning, Problem-based Learning, Structured Problem-solving, will be used.
The teaching method that will be used in this course will be composed of a series of mini lectures interrupted with
frequent discussions and brainstorming exercises. PowerPoint presentations will be prepared for the course materials.
A typical lecture would start with a short review (~ 5 minutes) using both PowerPoint presentations and the
blackboard. This review will also depend on discussions which will gauge the students’ digestion of the previous
material. Then, the students would have a lecture on new materials using PowerPoint presentations and blackboard.
The lecture presentation will be paused every 15 – 20 minutes with brainstorming questions and discussions that will
allow the students to reflect and think in more depth about what they learned in that presentation. Then, some
example problems will be presented and discussed with the students to illustrate the appropriate problem solving
skills that the students should learn. The lecture will be continued for another 15 – 20 minutes, followed by examples
and/or a quiz covering the materials taught in the previous two weeks.
4
Policy
Attendance
Class attendance is required and applied according to the university regulations (student’s guide
page 43). Data support the idea that class attendance improves learning. It is very difficult as well as
uninspiring for me to help a student who does not attend lectures. What is created in the classroom
cannot be reenacted.
Make-up tests will be done according to the university regulations. Please see student’s guide pages
44-45.
Homework
Working homework problems is an essential part of this course and they represent a key
opportunity to learn the subjects discussed. All homework problems assigned during a given week
are due at the beginning of class on the second meeting of the following week unless otherwise
stated. Late homework will not be accepted. Failure to turn in this particular homework on time
will result in a grade of 0 (zero) for the homework contribution to your final grade. Team work is
encouraged; however, the work one hands in must represent his/her own effort. Homework
solutions will be discussed in class. There will be no handouts of homework solutions.
Student Conduct
All University regulations apply to this course. In particular, the policies concerning academic
dishonesty and withdrawal from a course apply. May 3rd is the last day to withdraw. I will sign
drop slips without restriction.
Contribution of Course to Meeting the Professional Component
The course contributes to building the fundamental basic concepts Biomedical Engineering
ABET Category Content
Engineering Science
3.0 Credits
Engineering Design
5