—

df<-read.csv("df.csv")
table(df$rule_1)

## 
##  1  2  3  4 
## 53 53  6  2

table(df$rule_2)

## 
##  1  2  3  4 
## 46 58  8  2

table(df$rule_3)

## 
##  1  2  3  4 
## 45 55 10  4

table(df$rule_4)

## 
##  1  2  3  4 
## 47 56  7  4

table(df$rule_5)

## 
##  1  2  3  4 
## 54 48  8  4

str(df$rule_1)

##  int [1:114] 1 2 1 1 1 1 1 3 3 2 ...

df$rule=(df$rule_1+df$rule_2+df$rule_3+df$rule_4+df$rule_5)/5
table(df$Edu)

## 
##  1  2  3  4 99 
##  1 45 64  3  1

df$Edu[df$Edu==1]<-2
df$Edu[df$Edu==2]<-2
df$Edu[df$Edu==3]<-4
df$Edu[df$Edu==4]<-4
library(dplyr)

## 
## 다음의 패키지를 부착합니다: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

df<-df %>% filter(Edu!=99)
str(df$Edu)

##  num [1:113] 4 4 4 2 2 4 2 2 4 4 ...

df$Edu<-factor(df$Edu)
df$Task<-ifelse(df$Task=="1,2","1",df$Task)
df$Task<-ifelse(df$Task=="2,3","3",df$Task)
df<-df %>% filter(Task!=99)
df$Task<-factor(df$Task)
attach(df)
m<-lm(rule~harm+Career+Task,data=df)
summary(m)

## 
## Call:
## lm(formula = rule ~ harm + Career + Task, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.96246 -0.41341 -0.04441  0.30776  1.50234 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.03174    0.23163   8.772 3.03e-14 ***
## harm         0.10331    0.05155   2.004   0.0476 *  
## Career      -0.21594    0.04872  -4.432 2.26e-05 ***
## Task2       -0.01093    0.17301  -0.063   0.9497    
## Task3        0.37192    0.25045   1.485   0.1405    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5695 on 107 degrees of freedom
## Multiple R-squared:  0.2259, Adjusted R-squared:  0.1969 
## F-statistic: 7.805 on 4 and 107 DF,  p-value: 1.473e-05

plot(m)

library(car)

## Warning: 패키지 'car'는 R 버전 4.1.3에서 작성되었습니다

## 필요한 패키지를 로딩중입니다: carData

## 
## 다음의 패키지를 부착합니다: 'car'

## The following object is masked from 'package:dplyr':
## 
##     recode

vif(m)

##            GVIF Df GVIF^(1/(2*Df))
## harm   1.035570  1        1.017630
## Career 1.114086  1        1.055503
## Task   1.094218  2        1.022765