Fitting linear models
If your future role involves building predictive models, the interviewer might be interested in testing your knowledge of linear regression.
Linear regression models are one of the basic forms of predicting values for linearly related data. Linear regression model requires normality and homoscedasticity of the errors. If you fit a linear regression model during the interview, ensure that these assumptions are met.
You are already familiar with the cats dataset. The dataset is available in your environment.
To add a regression line to the plot, you can use abline() applied on a linear model's object.
Cet exercice fait partie du cours
Practicing Statistics Interview Questions in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Draw the scatterplot
___(___ ~ ___, data = ___)
# Fit the linear model
model <- ___(___ ~ ___, data = ___)
# Add the regression line
___(model)