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1. 2020-02-13 17:07:38 | Teaching Materials | Contributor(s): Matthew Aiello-Lammens | doi:10.25334/EM4Y-X154

In this lesson, students will have the opportunity to work through a chi-squared test of independence between two categorical variables.

2. 2020-02-12 20:27:04 | Teaching Materials | Contributor(s): Emily Weigel | doi:10.25334/NANC-M515

The students will learn to generate, test, and graphically represent basic hypotheses on data distributions using large datasets.

3. 2020-02-12 20:18:35 | Teaching Materials | Contributor(s): Mary E McWhirt, Emily Weigel | doi:10.25334/ABP7-GA97

The students will learn to estimate population sizes and consider assumptions of mathematical models and their applicability to the ecology of an organism/population

4. 2020-01-27 21:11:02 | Teaching Materials | Contributor(s): Emily Weigel, Alycia Lackey | doi:10.25334/N0T7-DD80

The students will practice identifying the appropriate basic statistical tests when given a scenario and learn how to run and interpret those statistical tests in R.

5. 2020-01-13 20:13:06 | Teaching Materials | Contributor(s): Marney Pratt | doi:10.25334/FM8B-PM89

This lesson helps students know some of the options for how to graph grouped continuous data (such as those involved in doing a t-test or ANOVA) and how to choose the best option.

6. 2020-01-13 20:00:00 | Teaching Materials | Contributor(s): Kevin Geyer | doi:10.25334/92KV-EH29

This lesson offers an introduction to ANOVA, including 1) how this statistical test can be differentiated from others and 2) a step-by-step guide to conducting and interpreting ANOVA results in R, including assumption testing and post-hoc analysis.

7. 2020-01-13 19:37:01 | Teaching Materials | Contributor(s): Jeremy Claisse | doi:10.25334/SA4D-PF95

Interpreting One-Factor Analysis of Variance (ANOVA)

8. 2020-01-13 19:30:34 | Teaching Materials | Contributor(s): Bill Morgan | doi:10.25334/PQ79-N018

How to use the "three-prong" approach to check for normality

9. 2020-01-13 19:18:39 | Teaching Materials | Contributor(s): Bengt Allen | doi:10.25334/2MRV-SA62

One-way analysis of variance (ANOVA), following the text in Chapter 5 of Beckerman et al. (2017) Getting started with R: an introduction for biologists, 2nd edition.

10. 2020-01-01 21:47:29 | Teaching Materials | Contributor(s): Daniel Lauer, Emily Weigel | doi:10.25334/ABY7-GQ05

The students will practice the basic code to test MacArthur and Wilson’s (1967) Island Biogeography model, focusing on how island size, distance, and perturbation affect species numbers.

11. 2020-01-01 21:18:30 | Teaching Materials | Contributor(s): Mary Kho, Emily Weigel | doi:10.25334/GZCA-S726

The students will practice manipulating data to add to or extract subsets of specific values, rows, columns, or subsets of data contained in existing data files.

12. 2019-10-08 18:15:05 | Teaching Materials | Contributor(s): Mary Elizabeth McWhirt, Emily Weigel | doi:10.25334/2YEK-ZY02

This lesson centers around the marginal value theorem (MVT, Charnov 1976), which describes how animals should forage in patches. It serves as a pre-lab to teach MVT basics, vectors, ANOVA, and basic plotting.

13. 2019-05-15 15:14:22 | Teaching Materials | Contributor(s): Caitlin Hicks Pries | doi:10.25334/Q4KF2V

The goal of this lesson is to learn how to import datasets into R, understand variable types, make adjustments to variables, perform basic calculations, and begin data visualization. The exercise uses an over 100 year time series of climate data.

14. 2019-05-10 21:50:18 | Teaching Materials | Contributor(s): Rachel Hartnett | doi:10.25334/Q4KJ04

There are two complementary lessons. The first covers how sampling distributions are made and ID's their key properties. The second covers how null distributions and how the test statistic of a dataset and its p-value are visualized on this distribution.

15. 2019-05-10 16:05:13 | Teaching Materials | Contributor(s): Rachel Hartnett | doi:10.25334/Q4W161

This short swirl lesson on importing data is designed to 1) get a basic understanding in how tables are read into R and some common issues and 2) develop an individual set of instructions for students to use later to import a table on their own.

16. 2019-01-23 21:59:44 | Teaching Materials | Contributor(s): Darcy Taniguchi | doi:10.25334/Q4H44W

This Swirl course uses ecological data of bird counts and habitat variables to estimate inter-observer variability and the relationship of birds with habitat variables.

17. 2019-01-23 21:53:55 | Teaching Materials | Contributor(s): Austin Happel | doi:10.25334/Q4MT7V

Students work with example data to generate a length-weight plot and use lm() to obtain regression coefficients. Base plot functions are used to plot both the data and the regression line.

18. 2019-01-23 21:46:13 | Teaching Materials | Contributor(s): Nadia Aubin-Horth | doi:10.25334/Q4RM8D

Uses swirl to walk students through how to use R and analyze their own data.

19. 2019-01-23 21:41:27 | Teaching Materials | Contributor(s): Stephen Gosnell | doi:10.25334/Q4WB30

This lesson focused on introducing students to binomial data analysis and reinforcing concepts of confidence intervals, p-value interpretation, and one- vs two-sided tests.

20. 2019-01-23 21:18:11 | Teaching Materials | Contributor(s): Didem Ikis | doi:10.25334/Q45T6F

In this lesson, students will learn the necessary statistical terms to understand two-way ANOVA, how to visualize their data, how to perform two-way ANOVA and interpret their results, and how to check for the ANOVA assumptions.

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