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    Evolutionary Ecophysiology swirl lesson
    Uses swirl to walk students through how to use R and analyze their own data.
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    Binomial test
    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.
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    Population Ecology in Swirl: Estimating Population Sizes
    The students will learn to estimate population sizes and consider assumptions of mathematical models and their applicability to the ecology of an organism/population

    Keywords: RSwirl

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    Genome Sequence Data in R using Biostrings (Swirl Lesson)
    By the end of this lesson, students should be able to load FASTA files into R as DNAStringSets and use width() and alphabetFrequency(), combined with other functions like sum() and mean(), to...
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    Checking Normality in R with swirl
    How to use the "three-prong" approach to check for normality
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    Hardy-Weinberg Equilibrium: a swirl resource
    Students will use swirl to understand Hardy-Weinberg equilibrium. The lesson starts with observed numbers of individuals for each genotype, and students will work through a number of steps to...
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    Importing Data into R
    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...
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    Avian Censys Analysis
    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.
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    Graphing grouped continuous data in R with swirl
    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.
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    Conducting Analysis of Variance (ANOVA) in R with swirl
    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,...
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    Interpreting one-factor ANOVA in R with swirl
    Interpreting One-Factor Analysis of Variance (ANOVA)
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    One-way ANOVA in R with swirl
    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.
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    Island Biogeography
    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.
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    Two-Way ANOVA
    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...
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    Tree Biomass and Phenology
    This lesson centers around understanding the connection of tree biomass and phenology with climate change. It uses both swirl and LearnR lessons in the R programming language to help students make...
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    Working with Datasets in R swirl
    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...
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    REMNet Tutorial, Excel Part 4: Focusing on Taxonomy 4.26.19
    Video on analyzing microbiome data in Excel from the Research Experiences in Microbiomes Network
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    Analyzing Nutrient Simulation Experiments with a one-way ANOVA
    We discuss what tests are appropriate to analyze the data from nutrient simulation experiments. They will conduct one-way ANOVAs and a post hoc test using a dummy dataset and interpret the results.
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    Sampling Distributions and Null Distributions: two swirl lessons in R
    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...
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    Basic Statistics
    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.
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