BIOL 391 Outline

THE KING'S UNIVERSITY

TERM:

2020-21 Fall

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
Advanced topics

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.