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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.
Midterm I15%
Midterm II(non-cumulative)15%
Final Exam (cumulative)25%
Laboratory Reports/Assignments15%
Laboratory Exam I 15%
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.
  • 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.

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