• 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?

—–
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.

> head(Salaries)
       rank discipline yrs.since.phd yrs.service  sex salary
1      Prof          B            19          18 Male 139750
2      Prof          B            20          16 Male 173200
3  AsstProf          B             4           3 Male  79750
4      Prof          B            45          39 Male 115000
5      Prof          B            40          41 Male 141500
6 AssocProf          B             6           6 Male  97000

> lm.sal.sex <- lm(salary ~ sex, data=Salaries)
> summary(lm.sal.sex)

Call:
lm(formula = salary ~ sex, data = Salaries)

Residuals:
   Min     1Q Median     3Q    Max 
-57290 -23502  -6828  19710 116455 

Coefficients:
            Estimate Std. Error
(Intercept)   101002       4809
sexMale        14088       5065
            t value Pr(>|t|)    
(Intercept)  21.001  < 2e-16 ***
sexMale       2.782  0.00567 ** 
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’
  0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 30030 on 395 degrees of freedom
Multiple R-squared:  0.01921,	Adjusted R-squared:  0.01673 
F-statistic: 7.738 on 1 and 395 DF,  p-value: 0.005667

> lm.sal.rank <- lm(salary ~ rank, data=Salaries)
> summary(lm.sal.rank)

Call:
lm(formula = salary ~ rank, data = Salaries)

Residuals:
   Min     1Q Median     3Q    Max 
-68972 -16376  -1580  11755 104773 

Coefficients:
              Estimate Std. Error
(Intercept)      80776       2887
rankAssocProf    13100       4131
rankProf         45996       3230
              t value Pr(>|t|)    
(Intercept)    27.976  < 2e-16 ***
rankAssocProf   3.171  0.00164 ** 
rankProf       14.238  < 2e-16 ***
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’
  0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23630 on 394 degrees of freedom
Multiple R-squared:  0.3943,	Adjusted R-squared:  0.3912 
F-statistic: 128.2 on 2 and 394 DF,  p-value: < 2.2e-16

> 

> summary(lm.sal.many)

Call:
lm(formula = salary ~ yrs.service + rank + discipline + sex, 
    data = Salaries)

Residuals:
   Min     1Q Median     3Q    Max 
-64202 -14255  -1533  10571  99163 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   68351.67    4482.20  15.250  < 2e-16 ***
yrs.service     -88.78     111.64  -0.795 0.426958    
rankAssocProf 14560.40    4098.32   3.553 0.000428 ***
rankProf      49159.64    3834.49  12.820  < 2e-16 ***
disciplineB   13473.38    2315.50   5.819 1.24e-08 ***
sexMale        4771.25    3878.00   1.230 0.219311    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 22650 on 391 degrees of freedom
Multiple R-squared:  0.4478,	Adjusted R-squared:  0.4407 
F-statistic: 63.41 on 5 and 391 DF,  p-value: < 2.2e-16

> 

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