https://github.com/bgreenwell/stt7140-env
Course materials for STT/ES 7140
https://github.com/bgreenwell/stt7140-env
Last synced: about 2 months ago
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Course materials for STT/ES 7140
- Host: GitHub
- URL: https://github.com/bgreenwell/stt7140-env
- Owner: bgreenwell
- Created: 2018-02-07T03:45:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-04-17T03:00:32.000Z (about 7 years ago)
- Last Synced: 2025-02-02T00:48:41.231Z (4 months ago)
- Language: HTML
- Size: 42.5 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
output:
md_document:
variant: markdown_github
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "misc/README-"
)
```
# ES/STT 7140: Statistical Modeling for Environmental Data 
This is an Environmental Statistics course that covers basic statistical methodologies encountered when collecting and analyzing data from biological surveys and experiments. Students in this class should have had at least one statistics course that covers the basic of probability, statistical thinking (e.g., variability), modeling, and inference. The notions of probability distributions (e.g., the **normal distribution**), hypothesis testing, test statistics, analysis of variance (ANOVA), $p$-values, and regression analysis should be familiar to students in this class.
### Topics to be covered include:
- Sampling design and probability surveys
- [Linear regression](https://github.com/bgreenwell/stt7140-env/blob/master/slides/ch3-regression.pdf) ([Homework 3](https://github.com/bgreenwell/stt7140-env/blob/master/homework/homework3.pdf); [Homework 4](https://github.com/bgreenwell/stt7140-env/blob/master/homework/homework4.pdf))
- Linear mixed-effects models
- Nonlinear regression
- Generalized linear models ([Homework 5](https://github.com/bgreenwell/stt7140-env/blob/master/homework/homework5.pdf); [Homework 6](https://github.com/bgreenwell/stt7140-env/blob/master/homework/homework6.pdf))
- Decision trees and random forests
- Time series (e.g., ARIMA models)
- Spatial statistics