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MCP8050C
Spring Semester 2015
Syllabus & Schedule
Statistics and Experimental Design for the Biomedical Sciences is a practical course designed to provide students
with a solid foundation and intuitive understanding of statistics for the biomedical sciences. The course covers good
practice in experimental design and statistical analysis. The course emphasizes parametric and nonparametric
statistics used in making between-group inferences, linear and nonlinear regression used in modeling physiological
phenomena, effective data presentation, and graphic integrity. This 3-credit-hour course comprises both lectures
and workshops.
Course Bryan Mackenzie, PhD (Email: bryan.mackenzie@uc.edu)
Director Tel: 513-558-3627 ● Office: MSB 4203 ● Office hours: Mondays 1:30 – 2:30 pm
Instructor John N Lorenz, PhD (Email: john.lorenz@uc.edu)
Tel: 513-558-3097 ● Office: MSB 4259 ● Office hours: By appointment
Teaching Jessica Ross, BA (Email: ross2j4@mail.uc.edu)
Assistants John Snedeker, BS (Email: snedekjr@mail.uc.edu)
Registration Course #
MCP8050C
GRADUATE
Section Call #
Credits
Class Schedule
Location
001
3G
Tuesdays 1:30 – 2:50 pm
Wednesdays 2:00 – 3:50 pm
MSB 4051
MSB 5051
304831
Assessment Assignments‡ and class participation (10%)
Mid-term exam: Multiple-choice test (25%)
Final exam part I: Multiple-choice test (30%)
Final exam part II: Practical test (35%)
‡Assignments will be administered via Blackboard. Late assignments will not be awarded credit.
Required participation by undergraduate (MCP5050C) and graduate students includes (1)
participating in class discussions on lecture days and (2) presenting solutions to problems given in
the weekly workshops. In addition, graduate students are required to prepare a written critique of
the experimental design, statistical methods and reporting in a published paper and present a
summary of that critique at Workshop 12.
Grading Grading will be in line with college of medicine policy with no adjustment for the distribution of
scores. There is no option for the remediation of grades after the scheduled final exams (i.e. there
is no make-up test).
A
A−
89.50%–100%
84.50%–89.49%
B+ 81.50%–84.49%
B
76.50%–81.49%
B− 73.50%–76.49%
C+ 69.50%–73.49%
C
66.50%–69.49%
Fail Below 66.5%
Prerequisites None
Attendance Attendance is required
Auditing Auditing requires advance permission of the Course Director
Web Page http://med.uc.edu/systemsbiology/studycourse/statistics.aspx
Blackboard & Enroll in meta_mackenb_505: (Meta 15SS) STATISTICS (001). NB: Messages sent via
Email Policy Blackboard will be considered sufficient notice. You should make sure that you have entered your
preferred email address in Blackboard under Tools → Personal Information → Edit Personal
Information.
Textbooks There is no required textbook for this course but reference to textbooks and online eTexts is highly
recommended as you study for this course. Some recommended eTexts are linked from the
Blackboard class under Web Resources → eTexts and Applets. Recommended textbooks include:
Philip Rowe (2007) Essential Statistics for the Pharmaceutical Sciences,
Wiley, Chichester
ISBN: 9780470034682 (paperback)
ISBN: 9780470034705 (hardback)
ISBN: 9780470319437 (e-book)
On Reserve at Health Sciences Library (call number QV 20.5 R879e 2007)
A very accessible, easy-to-read textbook Essential Statistics will help you
gain a solid understanding of statistics and good practice. Rowe walks the
reader through the most common statistical tests and is careful to point
out the many pitfalls that researchers can encounter.
Robert H. Riffenburgh (2013) Statistics in Medicine, 3e,
Elsevier/Academic Press, San Diego
ISBN: 9780123848642 (hardback)
ISBN: 9780123848659 (e-book)
Free online access (on-campus or connected to UC via VPN):
http://www.sciencedirect.com/science/book/9780123848642
A thorough and comprehensive statistics manual for biomedical and clinical
research, Statistics in Medicine will also serve as an excellent reference for
many of the tests that are beyond the scope of this course.
Workshop,
Practical
Exam, and
Required
Software
For the workshop and practical exam, you must bring a laptop
computer with SigmaPlot v12.5 or later installed. You can
purchase a site-licensed copy of SigmaPlot for $64 (incl tax) at
UC Bookstores; the one-year license expires August 1, 2015
(http://www.uc.edu/ucit/students/software/sigmaplot.html).
SigmaPlot requires the Windows operating system. In order to run SigmaPlot on your mac you will
have to either (1) use a Windows compatibility layer (e.g. CrossOver Mac) in which you can run
SigmaPlot, or (2) partition your disk (using Bootcamp) and install Windows on that partition (see
http://www.uc.edu/content/dam/uc/ucit/docs/helpdesk/InstallingWindows7UltimateOnAMac.pdf).
You can purchase Windows at UC Bookstores for $7 with your student ID. If you have an earlier
version of SigmaPlot, you may find it difficult to follow along in workshops. Previous builds of
version 12 contain serious bugs. If you do not have your own laptop or if you cannot install
Windows, you should contact your program coordinator or director as your program may be able
to lend you a laptop for the duration of the course.
Academic The University Rules, including the Student Code of Conduct, and other documented policies of the
Integrity department, college, and university related to academic integrity will be enforced. Any violation of
Policy these regulations, including acts of plagiarism or cheating, will be dealt with on an individual basis
according to the severity of the misconduct.
Special If you have any special needs related to your participation in this course, including identified visual
Needs Policy impairment, hearing impairment, physical impairment, communication disorder, and/or specific
learning disability that may influence your performance in this course, you should meet with the
instructor to arrange for reasonable provisions to ensure an equitable opportunity to meet all the
requirements of this course. At the discretion of the instructor, some accommodations may require
prior approval by Disability Services.
Statistics and Experimental Design for the Biomedical Sciences — MCP8050C
Spring Semester 2015
Class Meets: Tuesdays 1:30–2:50 pm in MSB 4051 and Wednesdays 2:00–3:50pm in MSB 5051
Date
Format
Topic
Instructor
Tues 13 Jan
Lecture 1
Introduction to Statistics I: Basic Concepts; Probability and Distributions
Mackenzie
Wed 14 Jan
Workshop 1
Probability and Probability Distributions; Introduction to SigmaPlot 13
Mackenzie
Tues 20 Jan
Lecture 2
Introduction to Statistics II: Descriptive Statistics; Hypothesis Testing
Mackenzie
Wed 21 Jan
Workshop 2
Descriptive Statistics; Hypothesis Testing
Mackenzie
Tues 27 Jan
Lecture 3
Between-Group Inferences I: Student’s t Tests (One-Sample, Two-Sample, Paired) Mackenzie
Wed 28 Jan
Workshop 3
Between-Group Inferences I: Student’s t Tests (One-Sample, Two-Sample, Paired) Mackenzie
Tues 3 Feb
Lecture 4
Between-Group Inferences II: Nonparametric Testing (Rank-Sum Test, SignedMackenzie
Rank Test, and Sign Test)
Wed 4 Feb
Workshop 4
Between-Group Inferences II: Nonparametric Testing (Rank-Sum Test, SignedMackenzie
Rank Test, and Sign Test)
Tues 10 Feb
Lecture 5
Between-Group Inferences III: Chi-Square Test, Fisher’s Exact Test, and Analysis
Mackenzie
of Frequencies; Odds Ratios and Relative Risk
Wed 11 Feb
Workshop 5
Between-Group Inferences III: Chi-Square Test, Fisher’s Exact Test, and Analysis
Mackenzie
of Frequencies; Odds Ratios and Relative Risk
Tues 24 Feb
Lecture 6
Between-Group Inferences IV: Analysis of Variance and Multiple Comparisons
Mackenzie
Wed 25 Feb
Workshop 6
Between-Group Inferences IV: Analysis of Variance and Multiple Comparisons
Mackenzie
Tues 3 Mar
Mid-Term Exam: Multiple Choice, 1:30–2:30 pm, MSB 4051
(Mid-Term Exam covers material from Lectures 1–6 and concepts from Workshops 1–6)
Wed 4 Mar
Lecture–
Workshop 7
False Discovery Rate; Permutation Methods; Normalization;
RT-qPCR Data Analysis; ROC Analysis
Ross /
Snedeker
Tues 10 Mar
Lecture 8
Experimental Design; Multifactorial Analysis
Lorenz
Wed 11 Mar
Workshop 8
Experimental Design; Multifactorial Analysis
Lorenz
Tues 17 Mar
Wed 18 Mar
Spring Break
Tues 24 Mar
Lecture 9
Power Analysis and Sample-Size Estimation; Survival Analysis
Lorenz
Wed 25 Mar
Workshop 9
Power Analysis and Sample-Size Estimation; Survival Analysis
Lorenz
Tues 31 Mar
Lecture 10
Correlation; Regression Analysis (Linear Regression; Multiple Linear Regression)
Ross
Wed 1 Apr
Workshop 10
Correlation; Regression Analysis (Linear Regression; Multiple Linear Regression)
Ross
Tues 7 Apr
Lecture 11
Nonlinear Regression; Model Improvements
Mackenzie
Wed 8 Apr
Workshop 11
Nonlinear Regression; Model Improvements
Mackenzie
Workshop 12
Critiquing Experimental Design and Statistical Analyses of Published Articles
Tues 21 Apr
Lecture 12
Data Reduction, Graphic Integrity and Effective Data Presentation
Mackenzie
Wed 22 Apr
Workshop 13
Review Workshop
Mackenzie
Tues 28 Apr
Final Exam Part I: Multiple Choice, 1:30–2:30 pm, MSB 5051
(Final Exam Part I covers material from the entire course with an emphasis on Lectures 7–12 and concepts from
Workshops 7–13)
Wed 29 Apr
Final Exam Part II: Practical, 2:00–3:30 pm, MSB 5051
(Final Exam Part II covers material from the entire course)
Tues 14 Apr
Wed 15 Apr