Statistical Modeling and Regression

Session 2: swirl()

Swirl

For this session, you will learn how to run regressions with the interactive course from swirl().

install.packages("swirl")
library(swirl)

Choose module 2: Regression Models: The basics of regression modeling in R.

There are 13 Topics in Regression! Each Topic takes around 15 minutes. Start with the Introduction.

Assignment

In this part, your assignment is to pratice some regression tasks. We expect you to be familar with regression model in R and can use it confidently.

Task 1: Creating a linear model in R

1. Downloading of the data is trivial today, as we will use a built-in dataset called iris

iris <- iris
head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Here is an illustration of the variables (image source: https://www.datacamp.com/community/tutorials/machine-learning-in-r):

2. Creating a linear model to explore the relationship between Sepal.Length and Petal.Length
3. Understanding the result of a linear fit
4. Visualizing the linear fit

Task 2: Creating a multiple linear regression

1. Add Species to the model from before, exploring the relationship between Sepal.Length and Petal.Length for different Species
2. Understanding the result of a linear fit
3. Visualizing the multiple linear fit