Biostatistics
Biol 458/558 Spring, 2002
By Dr. Weixing Zhu
Science III, Rm. 391 x 73218
wxzhu@binghamton.edu
Class hour and location: Tu, Th 1:05-2:40 (S2G35), Mon. 2:20 – 3:20 pm (Lab, Science
II computer pod, S2-134, no class first Monday)
Office hour: Before or after Tu, Th classes or by appointment
Text book: J.H. Zar 1999, Biostatistical Analysis, 4th edition, Prentice Hall, NJ
Biostatistics covers basic statistic topics including probability and distribution, experimental
design and hypothesis testing (t-test, ANOVA, and non-parametric statistics), plus linear
regression and contingency test. It includes lecture and lab sections. It is designed for biology
graduate students to learn basic theories and practical skills in statistics. It is also offered to senior
level undergraduate in biology or related majors to enhance their independent research skills.
Twice a week lectures will introduce the basic statistics theories and their applications in
biological studies. We will use mostly biological dataset to illustrate and demonstrate the
procedures to do statistics analyses. Statistics is about number, and the best way to deal with
number is playing with it! Practice is the key to master statistics knowledge. Homework will be
assigned for that purpose. Homework is due in the following week lab section.
Student is the center of the learning. You should ask questions in the lecture whenever necessary.
In particular, I reserve the lab section for you to discuss and solve questions together, in an
environment where you can use computer to help you calculating data and understanding
statistical concepts. Students are required to take turns to lead lab sections. It is expected that
every student will become “expert” of using software to solve 1-2 statistics problems. Forming
self-help groups to study together is highly recommended, although each person is expected to
finish homework by him/herself.
There will be three exams to test the knowledge learned in this class. No statistical software, no
group discussion is allowed in the exams. In addition, graduate students are required to submit a
paper (About 5 pages) containing hypothesis, data, data analysis, and conclusions.
Goals of the class
1. Learn statistical methods to analyze, summarize and present data.
2. Understand basic statistical theory to design your experiments.
3. Aware the assumptions of various statistical methods before experimental design and
data analysis.
4. Have FUN by crunching number and master a highly useful SKILL for your career!
Grade
Undergraduate: 15% homework assignment (including class participation), 15% 1st exam, 30%
mid-term, and 40% final.
Graduate: 15% homework assignment, 15% 1st exam, 30% mid-term, and 40% final. A term
paper is required and counted 25% towards the overall grade (due before the week of final).
Comments and Feedbacks: Please either talk to me directly or send me e-mail. In addition, we
will have a midterm course assessment in which you can provide your feedbacks to this class.
Course Schedule
Date
Content
Reading
Jan 29
Jan 31
***
Introduction; Introduce data distribution
Population and samples; Introduce central tendency
No lab class in the first week
Ch. 1
Ch. 2, 3.1-3.3
Feb 5
Feb 7
***
Measure data dispersion and variability
Introduce biodiversity index
First computer lab
4.1-4.6
4.7
Feb 12
Feb 14
Introduce probability
Introduce Normal Distribution
5.1-5.7
6.1-6.2
Feb 19
Feb 21
Continue normal distribution; Introduce statistical testing
First exam
6.3-6.5
Feb 26
Feb 28
Introduce one-sample analysis
Continue one-sample analysis
7.1-7.4
7.5-7.6, 7.14
Mar 5
Mar 7
Introduce two-sample analysis
Continue two-sample analysis
8.1-8.4
8.5, 8.7
Mar 12
Mar 14
Nonparametric statistics
Paired-sample analysis
8.9-8.10, 9.5
9.1-9.3
Mar 19
Mar 21
Introduce analysis of variance
Continue one-way ANOVA
10.1-10.2
10.3, 10.6
Mar 23-Apr 1
Spring Break
Apr 2
Apr 4
Nonparametric analysis of multiple groups
Midterm exam (cover contents through Mar 21)
10.4
Apr 9
Apr 11
Multiple comparison
Contrast analysis and confidence interval
11.1-11.2
11.3, 11.5
Apr 16
Apr 20
Two-way analysis of variance
Multiple comparisons
12.1-12.2
12.6
Apr 23
Apr 25
Randomized block experimental design
Data transformation
12.4
13.1-13.3
***
Apr 30
Topic outline for term paper due (for graduate students only)
Introduce linear regression
17.1-17.2
May 2
Continue linear regression
17.3-4, 19.10
May 7
May 9
Introduce linear correlation
Rank correlation
19.1-19.2
19.9
Final exam