• 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.

see hierarchical regression
see also statistical regression methods

• 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