Week 5 Workbook

Formative Exercise 5

#Graphic 1

This graphic can be associated to the MANOVA test. This is because “species” is a categorical predictor variable and “sepal length” is a quantitative outcome variable.

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + geom_boxplot()

#Graphic 2

This graphic can be associated with the Simple Regression test. The quantitative, predictive variable “petal length”, produces the single quantitative variable outcome of “petal length”.

ggplot(iris, aes(x = Petal.Length, fill = Species)) + geom_density()

#Graphic 3

This graphic can be associated with the Multiple Regression test. It shows a correlation between two predictive, quantitative variables “Petal Length” and “Petal Width” and produces an outcome comparing the correlations across the category “Species”.

ggplot(iris, aes(x = Petal.Length, y = Petal.Width)) + geom_point(size = 1, aes(color = Species)) + geom_smooth(method = "lm")

`geom_smooth()` using formula = 'y ~ x'

#Graphic 4

This graphic can be associated with the Chi-square test. Two categorical, predictor variables “size” and “species” produce the two outcome variables “size” and “species”.