UMD Biostatistics

Data Analysis and Statistics for Biological Sciences

Welcome to the course website for UMD Biostatistics. Here you’ll find the syllabus, schedule, lecture materials, activities, and assignments.

Welcome to Ecological Statistics at UMD!

This course teaches the fundamentals of statistics and data analysis for biological sciences. Using R and the tidyverse, you’ll learn how to analyze and visualize data effectively to answer scientific questions.

Course Materials

Materials for this course are organized into lectures, in-class activities, and homework assignments. All materials are available through this website and may be on canvas website at school.

Course Schedule

Week 1

01 - Introduction - Start with R

02 - Project Design and Graphing data - GGPLOT

Week 2

03 - Descriptive Stats and Wrangling

04 - Probability and Inference I - z distributions

Week 3

05 - Probability and Inference II - t distribution

06 - T-Tests

Week 4

07 - Non Parametric T-Tests

08 - Study Design for more than T-Tests - Sampling

Week 5

09 - Correlation vs Linear Models - Regression I

Week 6

10 - Multiple Regression

11 - Analysis of Variance

Week 7

12 - Analysis of Variance - 2 way ANOVA

13 - Analysis of Variance - nested ANOVAs

Week 8

14 - GLM and Logistic Regression - What if assumptions Fail

15 - ANCOVA - Analysis of Covariance

Week 9

16 - Multivariate Statistics

17 - Principal Component Analysis - PCA

Week 10

18 - NMDS - CLUSTER ANALYSIS - HYPOTHESES

19 - Review

Order of topics is subject to change depending on the progress of the class. Changes to the class schedule will be announced in class.

Readings

Course Schedule and Syllabus

I have put together a set of all statistical tests we have done

Below is a link to all the statistical tests that we are covering in the class. These should be helpful to accomplish most of the statistics you will need to do. The format of all of these tests is:

  • type of data for the test
  • hypotheses being tests
  • assumptions of the test
  • the test itself and interpretation of the output
  • tests of assumptions
  • how to make a final publication quality plot
  • how to write up the results of your output

Questions or Issues?

If you have questions about the course or the website, please contact me at wlperry@d.umn.edu

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