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# 1. Load necessary library
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# 2. Load the dataset (Use the English variable version)
# Ensure 'law_data2.csv' is in your working directory
df_law <- read.csv("law_data/law_data2.csv")

# 3. Data Preprocessing
# We rename the specific 'q_' columns to meaningful English names based on the analysis.
# Mapping:
# - q_122 : Trial Experience (재판 관련 경험 여부)
# - q_184 : Gender (성별)
# - q_185 : Age Group (연령별)
# - q_188 : Monthly Income (월 평균가구소득별)
# - q_83 ~ q_87 : Trust in Judicial System (Questions 82-86 in original)

df_analysis <- df_law %>%
  mutate(
    # (1) Create Trial Experience Dummy Variable
    # Assuming: 1 = Experience (Yes), 2 = No Experience (No)
    # Result: 1 = Yes, 0 = No
    Trial_Exp = ifelse(q_122 == 1, 1, 0),
    
    # (2) Create Average Trust Score (using q_83 to q_87)
    # These represent: Speed, Fairness, Bail Equality, Detention Equality, Wrongful Punishment
    Trust_System_Avg = rowMeans(select(., q_83, q_84, q_85, q_86, q_87), na.rm = TRUE),
    
    # (3) Rename demographic variables for clarity
    Gender = q_184,
    Age = q_185,
    Income = q_188
  )

# 4. Run Regression Analysis
# Hypothesis: Does Trial Experience affect Trust in the Judicial System?
model <- lm(Trust_System_Avg ~ Trial_Exp + Gender + Age + Income, data = df_analysis)

# 5. Print the Result
summary(model)
## 
## Call:
## lm(formula = Trust_System_Avg ~ Trial_Exp + Gender + Age + Income, 
##     data = df_analysis)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6862 -0.3072 -0.0491  0.3370  1.3844 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.531249   0.053425  47.379   <2e-16 ***
## Trial_Exp   -0.061104   0.031203  -1.958   0.0503 .  
## Gender       0.017000   0.018576   0.915   0.3602    
## Age          0.021015   0.007515   2.796   0.0052 ** 
## Income       0.011571   0.006153   1.881   0.0601 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5239 on 3395 degrees of freedom
## Multiple R-squared:  0.003892,   Adjusted R-squared:  0.002718 
## F-statistic: 3.316 on 4 and 3395 DF,  p-value: 0.01015

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