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ff BIOL 3117 - Biostatistics

Class Website
Related Links
B. Pokharel's Website

Class meeting

Monday and Wednesday 3:30-4:50 PM (A120)

Office hours

Monday and Wednesday (2:30-3:30pm) or by appointment

Course Description

This course will provide an overview of applying mathematical and statistical techniques to analyze biological data. The course will introduce students the basic statistical concepts including descriptive statistics, probability distributions, hypothesis testing, confidence intervals, analysis of variance (ANOVA) and simple linear regression. Students will be applying these techniques in real world biological problems. At the end, students will have an opportunity to learn skills in order to design experiments to test their own research questions, apply basic sampling theory to collect observational data, determine and conduct appropriate statistical test to make inferences or develop simple predictive model such as an allometric equation. The students will gain experience in using computer-based software (SPSS) to analyze biological data and interpret the results of statistical analyses.

Learning Objectives

The learning objectives of the courses are:
  • to provide foundation and motivation for students to develop interest in statistical thinking and data analysis techniques
  • to appreciate statistics as a tool for scientific research
  • to demonstrate ability to apply fundamental concepts in exploratory data analysis
  • to design experiments and/or observational studies to obtain high quality data to test research hypotheses
  • to understand the concept of sampling distribution, particularly describe the behaviour of sample mean collected from a population of unknown distribution
  • to demonstrate an appreciation of analysis of variance (ANOVA) in hypothesis testing
  • to fit a simple linear regression model such as allometric equation using a common statistical software package
  • to build competency using a common software package, and interpret, summarize and report its outputs for a scientific communication

Expectations or Outputs (6-8 which students will do in the course and I will measure)

By the end of the course, students will be able to:
  • use an appropriate software for an exploratory data analysis
  • identify common features in experimental design and also recognize some common types of sampling designs, such as simple random sampling, systematic sampling, and stratified random sampling
  • design research experiments or develop field data collection protocols to test research hypotheses
  • distinguish between a population and a sample, and also between parameters and statistics
  • describe properties of the sampling distribution of the sample mean using the central limit theorem
  • choose and justify appropriate statistical tests, and aware of their assumptions and limitations
  • identify situations where ANOVA is and is not appropriate, interpret an ANOVA table and perform the F test in ANOVA
  • fit a simple linear model to bivariate data using a common statistical software package
  • interpret, summarize and report outputs from the statistical tests using a common statistical software package

Course Resources

- Blackboard (need to login using your Webadvisor ID and Password)
- Website


Required Course Text:
  • Field, A. 2013. Discovering Statistics Using IMB SPSS Statistics. Fourth edition, Sage, Los Angeles.

Supplementary Textbooks and Materials:
  • Cunningham, J.B., and Aldrich, J.O. 2012. Using SPSS: An Interactive Hands-on Approach. SAGE, Los Angeles.
  • Emden, H.F.v. 2008. Statistics for Terrified Biologists. Second edition, Blackwell Publishing, Malden, Massachusetts.
  • Field, A., J. Miles and Z. Field. 2012. Discovering Statistics Using R. Third edition, Sage, Los Angeles.
  • Zar, J.H. 2010. Biostatistical Analysis. 5th edition, Pearson Prentice-Hall, Upper Saddle River, New Jersey.
  • Samuels, M.L., J.A. Witmer and A.A. Schaffner. 2012. Statistics for the Life Science. Prentice Hall, Boston.
  • Dytham, C. 2011. Choosing and Using Statistics: A Biologist's Guide. Third edition, Blackwell Publishing, Malden, Massachusetts.
  • Check the course website and blackboard regularly for further reading materials.

Course Schedule
(PDF Copy of Course Syllabus)

Week 1: (Sept 09 & 11)
Introduction and basic statistical concepts
Data, units and scales of measurement
Populations, samples, parameters and statistics
Occurrence, measures of central tendency, variability and dispersion
Bias, accuracy and precision

Example: frequency distribution and descriptive statistics by cross tabulation

Week 2: (Sept 16 & 18)
Data collection, experimental design and sampling techniques
Design of experiments: experimental units, treatments, factors, control groups, randomization, replication and blocking
Observational studies: preferential sampling, simple random sampling, systematic sampling, stratified random sampling, sampling intensity

Example: Comparison between simple random sampling and stratified sampling

Week 3: (Sept 23 & 27)
Probability distributions and sampling distribution
Normal distribution
Sampling distribution
Standard error and confidence intervals

Week 4: (Sept 30 & Oct 02)
Hypothesis testing
Hypothesis testing: formulate hypotheses, type I error, type II error
Inferences on single population mean
Assumptions: normality and equal variance

Week 5: (Oct 07 & 09)
Hypothesis testing continue
Comparison of two independent samples
Comparison of paired samples (paired t-test)
Non-parametric test

Example: t-test and z-test using table and SPSS (comparison using same set of data)

READING BREAK (October 14 - 18)

Week 6: (Oct 21 & 23)
Oct 23: Mid-term (20%)

Problem set I due before the class (Oct 21; NO LATE SUBMISSION)
Review for the mid-term

Week 7: (Oct 28 & 30)
Analysis of Variance (ANOVA)
Introduction to ANOVA
One-way ANOVA

Week 8: (Nov 04 & 06)
Analysis of Variance (ANOVA) continue

Example: ANCOVA using SPSS

Week 9: (Nov 11 & 13)
Analysis of Variance (ANOVA) continue
two-way ANOVA and factorial design
Summary of ANOVA

Example: Factorial design in SPSS

Week 10: (Nov 18 & 20)
Categorical data
Chi-squared test
Assumptions of chi-square test

Week 11: (Nov 25 & 27)
Linear regression and correlation
Simple linear regression
Fit statistics

Example: Fitting simple linear model using SPSS

Week 12: (Dec 02 & 04)
Multiple linear regression
Fitting a multiple regression model
Generalized linear model (GLM)
Review for the final exam

Example: Comparison between ANOVA, ANCOVA and GLM

Problem set II due before the class (Dec 04, 2013; NO LATE SUBMISSION)