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Bio300b logistics
The who, what, where and when of Bio300b
Lecture 1: Data lifecycle
Following data from birth, trying to avoid data death in the data lifecycle.
Lecture 2: Using R
An introduction to using R.
Lecture 3: Data manipulation
Using the tidyverse to load, tidy and process data.
Lecture 4: Data visualisation
Using ggplot2 to make beautiful figures.
Lecture 5: Experimental design
Lecture 6: Descriptive statistics
Lecture 7: Linear regression
Correlations and linear regression with continuous and categorical predictors.
Lecture 8: Multiple linear regression
For models with more than one predictor, you need multiple regression.
Lecture 9: Generalised linear models
Got Poisson or binomial data? Use Generalised linear models.
Lecture 10: Mixed effect models
Clustered data? You need to use a mixed effect model.
Lecture 11: Survival Analysis & multivariate analysis
Do you data show the time or distance until event happens? Maybe you need survival analysis.
Or if you have many response variables (such as species community data), you might use some multivariate methods. These methods are covered in more detail in Bio303 Ordination and Gradient Analysis.
Lecture 12 Non-linear analyses and course summary
If you have a non-linear relationship between your predictors and response, you might need non-linear regression