The search for the best model
You will start with a full model that predicts professor score based on rank, ethnicity, gender, language of the university where they got their degree, age, proportion of students that filled out evaluations, class size, course level, number of professors, number of credits, average beauty rating, outfit, and picture color.
Note you do not included the pic_outfit
or pic_color
variables in the full model because the original study states that these variables were used in a different analysis evaluating whether they're related to how highly the six students involved in the study score the professors' beauty (not related to how the students evaluate their professors in class).
This exercise is part of the course
Data Analysis and Statistical Inference
Exercise instructions
- Before running the model, think about which variable you would expect to have the highest p-value in this model and why.
- Run the full model, the code is displayed in the editor.
- Print the summary of the
m_full
model.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# The evals data frame is already loaded into the workspace
# The full model:
m_full <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval
+ cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals)
# View the regression output: