• 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 that SelvesLoc 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 or stepAIC (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?

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위의 Salaries 데이터사용이 안 될 때

• download to R from here salaries.csv

Salaries <- read.csv("http://commres.net/wiki/_media/salaries.csv")

• for information about Salaries (it may not be loaded),

??Salaries

Common topics

• What affects students GPA? Or what determines students' GPA?

Group topics

Questions you submit at the ajoubb.
Then we will list questions in Google docs Google survey