### COURSE NUMBER: BIOL 391
COURSE TITLE: Biostatistics
NAME OF INSTRUCTOR: Dr. Darcy Visscher
CREDIT WEIGHT AND WEEKLY TIME DISTRIBUTION: credits 3(hrs lect 3 - hrs sem 0 - hrs lab 3)
COURSE DESCRIPTION: An introduction to the design of experiments and analysis of data collected from field and laboratory studies in biology. Statistical software will be used extensively.

Prerequisites: BIOL 320, 330, STAT 300
REQUIRED TEXTS: The Analysis of Biological Data, by Whitlock and Schluter.  1 st  Edition 2008.
MARK DISTRIBUTION IN PERCENT:
 Midterm I 15% Midterm II(non-cumulative) 15% Final Exam (cumulative) 25% Assignments 15% Laboratory Reports/Assignments 15% Laboratory Exam I 15% 100%
COURSE OBJECTIVES: The course objectives are:
• To  demonstrate  an understanding  of probability theory as it applies to scientific data.
• To understand the role of statistics in biological research.
• To know the assumptions and shortcomings of particular statistical models.
• To  be  able  to  identify the  type  of statistical  model appropriate for  the  sampling design and the kind of data that should be collected.
• To be able to interpret the output of statistical analyses.
• To  be  able to design optimal  experiments and sampling  programs with the best possible use of limited time and resources.
The objectives of the laboratory component of the course are:
• To enhance the students’ understanding of the concepts covered in the lectures by gaining  experience  in  conducting  statistical  analyses and  designing  experiments and sampling procedures.
• To become proficient at using statistical software for analyses.
COURSE OUTLINE:
• Philosophy, introduction and history
• Probability distributions and estimation
• Statistical hypothesis testing
• Graphical data exploration
• Linear regression and correlation
• Multiple linear regression
• Experimental design
• Analysis of variance
• Multifactor analysis of variance
• Analysis of covariance
• Multivariate analysis
• Computational statistics
LAB OUTLINE:
• exploratory data analysis, normal distributions, t-statistics
• t-statistic, non-parametric test, linear regression
• transformations, linear regression, problem solving
• multiple linear regression I
• multiple linear regression II
• one-way ANOVA and power analysis
• multifactor ANOVA
• nested ANOVA and covariance
• multivariate analysis
• computational statistics

Required texts, assignments, and grade distributions may vary from one offering of this course to the next. Please consult the course instructor for up to date details.