STAT231B
Statistics for Biologists

1.  GENERAL INFORMATION

2.  COURSE DESCRIPTION

--  Goals

The two main goals of the course are: To achieve these goals, the instructor will present the basic techniques of multiple linear regression analysis, nonlinear regression analysis, multi-factor analysis of variance and analysis of some specialized experiment designs; the students will master all these techniques through literature reading, homework assignment, exams and projects.

--  Textbooks

Kutner, Michael H., Chris J. Nachtsheim, John Neter, and William Li.  2005. Applied Linear Statistical Models with Student CD-rom.  5th edition. 

Cody, Ronald P., Smith, Jeffrey K.  2006. Applied Statistics and the SAS programming language.  5th edition. 

The first book is required textbook that will be covered by lectures. The second book is reference book for SAS programming language that will be useful for doing homework and projects. I have reserved these two books in science library for two hours use. These books are highly recommended for your future references.
 

3.  COURSE REQUIREMENTS

Grades will be based on: More details follow.

--  Homework

There are weekly homework assignments, consisting of data analysis, interpretation and theoretical problems. The assignments are due each Tuesday by 8:10am. Students are allowed and encouraged to collaborate with others in doing the assignment.  A group of collaborating students ( no more than 3, however ) may turn in a single copy of the assignment with the names of all participants.  Each participant will receive the same grade for that assignment.  Alternatively you can turn in your own copy of the assignment if you work on the problems on your own. No late homework will be accepted for credit. For homework assignments please see

--  Computing Lab

The Wednesday lab session ( 3 hours: 4:10-7:00pm ) will be used to introduce SAS procedures with examples to help students to get started on homework. The step by step instruction for lab exercises will be available online every Wednesday morning. The TA will be available in this 3 hours lab session to help you with lab exercises and homework. For lab instructions please see

-- Project

Each student will choose one of the projects provided by the instructor as his/her focus project. These projects are focused on the areas of multiple linear regression analysis, nonlinear regression analysis, multifactor analysis of variance and specialized study designs. You can also choose your own data and research questions that fit in the above focused areas. Each student will also choose one of the above projects as his/her comments/critique project. Starting from fifth week, we will have one sessions of project presentation on every Tuesday. The focus groups who will present on Tuesday should submit a report to the instructor and their comments/critique groups by 12:00pm noon on Friday of the week before. On the presentation day, each session consists of three parts that will take about 40 minutes and go as follows: The grade for the project is based on your report (1/3), the presentation/defense of your project (1/3) and the comments/critiques on the project of the other group (1/3). For projects information please see

-- Exam

The final is in-class exam based on multiple-choice questions.  This exam is closed-books but students can bring a "cheat-sheet" no longer than 2 sheet front & back (= 4 pages) for the final.  For more information on materials covered by the exam see

4.  CLASS SCHEDULE & READINGS & LECTURE NOTES

ALSM5e = Applied Linear Statistical Models 5e (2005)
ASSPL5e = Applied Statistics and the SAS Programming Language 5e (2006)
Readings are indicated by section numbers.  For example, ALSM5e 6.1 means Chapter 6, Section 1. 
 
Class Date Subject Lecture note Readings
1 4-Apr (Tue)
  • Introduction to the class.
  • Matrix Algebra
 Matrix Algebra ALSM5e 5.1-5.7;  
--o--
2 6-Apr (Thu)
  • Matrix Algebra
 Matrix Algebra ALSM5e 5.1-5.7;  
--o--
3 11-Apr (Tue)
  • Matrix Regression for Simple Linear Regression
 Matrix Algebra for Simple Linear Regression ALSM5e 5.8-5.13;  
--o--
4 13-Apr (Thu)
  • Multiple Regression I
 Multiple Regression I ALSM5e 6.1-6.9; 
--o--
5 18-Apr (Tue)
  • Multiple Regression I
 Multiple Regression I ALSM5e 6.1-6.9; 
--o--
6 20-Apr (Thu)
  • Multiple Regression I
 Multiple Regression I ALSM5e 6.1-6.9; 
--o--
7 25-Apr (Tue)
  • Multiple Regression II
 Multiple Regression II ALSM5e 7.1-7.6; 
--o--
8 27-Apr (Thu)
  • Regression Models for Quantitative and Qualitative Predictors
 Regression Models for Quantitative and Qualitative Predictors ALSM5e 8.1-8.7; 
--o--
9 2-May (Tue)
  • Model Selection I
 Model Selection I ALSM5e 9.1-9.6 
--o--
10 4-May (Thu)
  • Model Selection I-cont'
 Model Selection I -cont' ALSM5e 9.1-9.6 
--o--
11 9-May (Tue)
  • Multiple Regression Diagnostics
 Multiple Regression Diagnostics ALSM5e 10.1-10.6 
--o--
12 11-May (Thu)
  • Multiple Regression Diagnostics
 Multiple Regression Diagnostics ALSM5e 10.1-10.6 
--o--
13 16-May (Tue)
  • Logistic Regression Model Fitting
 Logistic Regression Model Fitting ALSM5e 14.1-14.5 
--o--
14 18-May (Thu)
  • Logistic Regression Model Selection and Model Diagnosis
 Logistic Regression Model Selection and Model Diagnosis ALSM5e 14.6-14.10 
--o--
15 23-May (Tue)
  • Logistic Regression Model Selection and Model Diagnosis con't
 Logistic Regression Model Selection and Model Diagnosis con't ALSM5e 14.6-14.10 
--o--
16 25-May (Thu)
  • Logistic Regression Model for nominal or ordinal response and poisson regression
 Logistic Regression Model for nominal or ordinal response and poisson regression ALSM5e 14.6-14.10 
--o--
17 30-May (Tue)
  • Multi-Factor Studies
Multi-Factor Studies ALSM5e 19.1-19.11 
--o--
18 1-June (Thu)
  • Random and Mixed Effects
Random and Mixed Effects ALSM5e 25.1-25.5 
--o--
12 11-May (Thu)
  • Nested Designs
Nested Designs ALSM5e 26.1-26.7  
--o--
13 16-May (Tue)
  • Repeated Measures and Related Designs
Repeated Measures and Related Designs ALSM5e 27.1-27.6  
--o--