Introduction to Biological Statistics Course

Introduction to Biological Statistics Course

This space will be used to communicate with students in the Introduction to Biological Statistics Course. Here, you will find copies of lecture materials, exercises and other relevant resources. For more information, contact Nathaniel Pope at npope@coa.edu, Spencer Fox at spncrfx@gmail.com, or Nichole Bennett at nichole.lynn.bennett@gmail.com. If you want to get weekly announcements about the course (weekly topics, software requirements, etc.) please sign up for the listserv here.

The goal of this workshop is to provide graduate students early in their studies with a broad set of practical statistical knowledge and tools for their research projects. The workshop focuses on statistical analysis in R, and we provide basic R instruction that assumes no prior familiarity with R. Past workshops have included broad overviews and workable examples of the following types of analysis: linear models and model fitting, time series analysis, spatial statistics, phylogenetics, population genetics, population dynamics and principal components analysis. This workshop is not meant replace formal course work in statistics. Instead, it provides participants with a foundation of knowledge that can be built upon by future study.

The course has a GitHub repository which will periodically be updated with scripts and other materials that are used in the class. An annotated list of Statistics Resources is available on Google Drive.

This course meets Fridays 2-3:30 pm in GDC 7.514 .

Please take the course survey to help us better meet your needs! Also, we are actively looking for post-docs and graduate students to lead individual sessions. You don't have to be an expert, just willing to share what you know.

Prerequisite R knowledge assumed for statistics topics lectures:

If you're attending any of the specific statistics topic lectures, we expect that you have a reasonable understanding of the material presented in the first few weeks of class. We also expect that you can do the following in R: access help files for functions, load data, and install R packages. If you need extra practice/instruction in loading data or installing packages, we have the following cheat sheets for you. 

 

Week

Date

R Topic

Statistical Topic

Instructor

Week

Date

R Topic

Statistical Topic

Instructor

1

9/4

Introduction to R

Did we mention R?

Nate Pope/Spencer Fox/Nichole Bennett

2

9/11

Probability Distributions, Simulation

Probability and Likelihood

Nate Pope/Spencer Fox/Nichole Bennett

3

9/18

Functions, Flow Control

Hypothesis Testing

Nate Pope/Spencer Fox/Nichole Bennett

4

9/25

Model Fitting, Debugging

Linear Models

Nate Pope/Spencer Fox/Nichole Bennett

5

10/2

 

Model Building

Andrius Dagilis

6

10/9

Package lme4, parametric bootstrap

Mixed/Hierarchical Models

Nate Pope

7

10/16

 

Spatial/Temporal statistics

Emlyn Resetarits

8

10/23

Bayesian inference in JAGS

Bayesian Inference

Spencer Fox

9

10/30

Best programming practices in R

R, R, R, R, R, R, R...

Nichole Bennett

10

11/6

Package igraph

Networks

Amanda Perofsky

11

11/13

Package lme4, parametric bootstrap

Mixed/Hierchical Models

Nate Pope

12

11/20

Parallelization in R, R on TACC

R, TACC

Dennis Wylie

13

12/4

Best programming practices in R

R!

Nichole Bennett


Peer-led Introduction to Biological Statistics by https://utexas.atlassian.net/wiki/display/CCBB/Introduction+to+Biological+Statistics+Course is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.