BIOL 373 (Fall)

Biometry

 Instructor(s): B. Leung N6/13 (514) 398-6460 brian.leung2@mcgill.ca Workload: 3 credits (2-2-5) Prerequisite(s): MATH 112 or equivalent. Content: The aim of this course is to introduce students to the foundations of the analysis of biological data,  while emphasizing the assumptions behind statistical tests and models. I shall not as in the mathematical statistics course, go into detail about the specific mathematical derivations.  The course is designed to give a student the ability to intelligently use the statistical techniques typically available on computer packages such as SYSTAT or SPSS. APPROXIMATE ORDER OF TOPICS COVERED Course introduction; Introduction to data presentation Populations and samples; Pseudoreplication; Central tendency and variability Introduction to probability Normal distribution Introduction to hypothesis testing One-sample hypotheses Two-sample hypotheses:  the t-test Non-parametric statistics: Mann-Whitney; Data transformation Paired-sample hypotheses Multi-sample hypotheses: single-factor ANOVA Multiple comparisons Power, sample size, and assumptions in ANOVA; non-parametric ANOVA: Kruskal-Wallis test Two-Factor ANOVA with equal replication Two-Factor ANOVA: theory and multiple comparisons. Two-Factor ANOVA without replication; Randomized block; Repeated measures Hierarchical ANOVA, MANOVA Linear Regression Linear Regression; Hypothesis testing Multiple regression, Polynomial Regression, ANCOVA Goodness of fit: Chi-square Contingency tables Advanced topics You may not be able to get credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar. Check out: http://www.mcgill.ca/study/2013-2014/faculties/science/undergraduate/ug_sci_course_reqs#booknode-52063 Readings: J.H.Zar. Biostatistical Analysis, 5th ed., 2009.  Sokal, R.R., and F.J. Rohlf. 1995.  Biometry, 3rd ed., W.H. Freeman & Co. (Not available in book store but can be ordered) Method: Lectures and labs. Evaluation: Labs, class assignments, and a final exam.

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Last update: March 22, 2019