c:ma:2019:multiple_regression_exercise
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Table of Contents
Class A
- Install packages ISLR
- use a dataset, Carseats
- Build regression models with a DV, sales and IVs, your choices
- Use
?Carseats
command for the explanation of the dataset - Use
str
function to see the characteristic of each variable. Make it sure thatSelvesLoc
variable should be factor, not int or anything.
- Make a full model (with all variables) then reduce down the model until you find it fitted.
- Make a null model (with no variables) then, build up the model with additional IVs until you find a fitted model.
- Can we use
step
orstepAIC
(MASS package needed) function? - Interpret the result
step(lm.full, direction=“back”)
- Install packages tidyverse
- load the tidyverse
install.packages(“car”)
data(“Salaries”, package = “car”)
- Use a dataset Salaries
- describe the data set
- Regress sex variable on salary variable
- Write the regression model
- Discuss the difference
- Use rank variable for the same purpose
- –
- Use yrs.service + rank + discipline + sex
- on salary
- How do you interpret the result?
—-
위의 Salaries 데이터사용이 안 될 때
- download to R from here salaries.csv
Salaries <- read.csv("http://commres.net/wiki/_media/salaries.csv")
Discussion
Common topics
- What affects students GPA? Or what determines students' GPA?
Group topics
Making Questionnaire
Questions you submit at the ajoubb.
Then we will list questions in Google docs Google survey
c/ma/2019/multiple_regression_exercise.1573080022.txt.gz · Last modified: 2019/11/07 07:40 by hkimscil