|NAME OF INSTRUCTOR:
||Dr. Darcy Visscher|
|CREDIT WEIGHT AND WEEKLY TIME DISTRIBUTION:
||credits 3(hrs lect 3 - hrs sem 0 - hrs lab 3)|
||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
||The Analysis of Biological Data, by Whitlock and Schluter. 1 st Edition 2008.|
|MARK DISTRIBUTION IN PERCENT:||
|Final Exam (cumulative)||25%|
|Laboratory Exam I ||15%|
||The course objectives are:
The objectives of the laboratory component of the course 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.
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.
be able to design optimal experiments and sampling
programs with the best possible use of limited time and resources.
enhance the students’ understanding of the concepts covered in the
lectures by gaining experience in conducting
statistical analyses and designing experiments and
- To become proficient at using statistical software for analyses.
- 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
- Advanced topics
- 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.