c:ma:2019:multiple_regression_exercise

- 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. - 변인설명을 토대로 가설만들기
- 종속변인 = Sales
- 독립변인 = 숫자변인 1 + 종류변인 1 (조별 선택)
- Multiple regression without interactin
- Multiple regression with interaction

- 가설 만들기
- 종속변인 Sales
- 독립변인 여러개 (interaction 없이)
- Modeling 해 볼 것

see hierarchical regression

see also statistical regression methods ← 많이 쓰이지 않음

~~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")`

`install.packages("carData")`

- load the car and the carData
`data("Salaries", package = "carData")`

`?Salaries`

- explain what it is
- describe the data set

—-

- Regress sex variable on salary variable
- Write the regression model
- Discuss the difference male and female (sex)

- Use rank variable for the same purpose
- Write the regression model

- Regress rank + sex on salary
- How do you interpret the result?
- And regress rank + sex + rank:sex on salary
- How do you interpret this result?
- Do factorial ANOVA test with rank and sex on salary
- How do you interpret the result?

- Test regression model of your own choice
- Interpret the result

위의 Salaries 데이터사용이 안 될 때

- download to R from here salaries.csv
- use to import the data set.
`Salaries <- read.csv("http://commres.net/wiki/_media/salaries.csv")`

- for information about Salaries (it may not be loaded),
- use
`??Salaries`

to describe the data set.

—–

Please copy and paste the proper r command and output to a txt file (use notepad or some other text editing program). You could use MS Word, but, please make it sure that you use type-setting fonts such as “Courier New.” The below output, as an example, includes the r command `head(Salaries)`

and the output.

<code>

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

c/ma/2019/multiple_regression_exercise.txt · Last modified: 2021/11/11 10:14 by hkimscil